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ORCIDNada Lavrac
Nada Lavrač
2020 – today
- 2025
[j99]Boshko Koloski, Nada Lavrac, Blaz Skrlj:
HorNets: learning from discrete and continuous signals with routing neural networks. Mach. Learn. 114(4): 101 (2025)- [c132]Bojan Cestnik
, Andrej Kastrin, Boshko Koloski, Nada Lavrac:
Make Literature-Based Discovery Great Again Through Reproducible Pipelines. IDA 2025: 261-273 - [i23]Boshko Koloski, Nada Lavrac, Blaz Skrlj:
HorNets: Learning from Discrete and Continuous Signals with Routing Neural Networks. CoRR abs/2501.14346 (2025) - [i22]Bojan Cestnik, Andrej Kastrin, Boshko Koloski, Nada Lavrac:
Make Literature-Based Discovery Great Again through Reproducible Pipelines. CoRR abs/2502.16450 (2025) - [i21]Andraz Repar, Nada Lavrac, Senja Pollak:
Extracting domain-specific terms using contextual word embeddings. CoRR abs/2502.17278 (2025) - 2024
- [c131]Boshko Koloski, Nada Lavrac, Bojan Cestnik, Senja Pollak, Blaz Skrlj, Andrej Kastrin:
AHAM: Adapt, Help, Ask, Model Harvesting LLMs for Literature Mining. IDA (1) 2024: 254-265 - [e12]Slawomir Nowaczyk, Przemyslaw Biecek, Neo Christopher Chung, Mauro Vallati, Pawel Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomás Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczyslaw Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova:
Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part I. Communications in Computer and Information Science 1947, Springer 2024, ISBN 978-3-031-50395-5 [contents] - [e11]Slawomir Nowaczyk, Przemyslaw Biecek, Neo Christopher Chung, Mauro Vallati, Pawel Skruch, Joanna Jaworek-Korjakowska, Simon Parkinson, Alexandros Nikitas, Martin Atzmüller, Tomás Kliegr, Ute Schmid, Szymon Bobek, Nada Lavrac, Marieke Peeters, Roland van Dierendonck, Saskia Robben, Eunika Mercier-Laurent, Gülgün Kayakutlu, Mieczyslaw Lech Owoc, Karl Mason, Abdul Wahid, Pierangela Bruno, Francesco Calimeri, Francesco Cauteruccio, Giorgio Terracina, Diedrich Wolter, Jochen L. Leidner, Michael Kohlhase, Vania Dimitrova:
Artificial Intelligence. ECAI 2023 International Workshops - XAI³, TACTIFUL, XI-ML, SEDAMI, RAAIT, AI4S, HYDRA, AI4AI, Kraków, Poland, September 30 - October 4, 2023, Proceedings, Part II. Communications in Computer and Information Science 1948, Springer 2024, ISBN 978-3-031-50484-6 [contents] - [i20]Luka Andrensek, Boshko Koloski, Andraz Pelicon, Nada Lavrac, Senja Pollak, Matthew Purver:
Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis. CoRR abs/2409.20054 (2024) - 2023
- [j98]Blaz Skrlj, Matej Bevec, Nada Lavrac:
Multimodal AutoML via Representation Evolution. Mach. Learn. Knowl. Extr. 5(1): 1-13 (2023) - [c130]Boshko Koloski, Marko Pranjic, Nada Lavrac, Blaz Skrlj, Senja Pollak:
Inducing Document Representations from Graphs: A Blueprint. Tiny Papers @ ICLR 2023 - [i19]Jan Kralj, Blaz Skrlj, Ziva Ramsak, Nada Lavrac, Kristina Gruden:
DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks. CoRR abs/2302.03907 (2023) - [i18]Boshko Koloski, Blaz Skrlj, Senja Pollak, Nada Lavrac:
Latent Graph Powered Semi-Supervised Learning on Biomedical Tabular Data. CoRR abs/2309.15757 (2023) - [i17]Boshko Koloski, Nada Lavrac, Bojan Cestnik, Senja Pollak, Blaz Skrlj, Andrej Kastrin:
AHAM: Adapt, Help, Ask, Model - Harvesting LLMs for literature mining. CoRR abs/2312.15784 (2023) - 2022
- [j97]Tadej Skvorc, Nada Lavrac, Marko Robnik-Sikonja:
NeSyChair: Automatic Conference Scheduling Combining Neuro-Symbolic Representations and Constrained Clustering. IEEE Access 10: 10880-10897 (2022) - [j96]Blaz Skrlj, Jan Kralj, Janez Konc, Marko Robnik-Sikonja, Nada Lavrac:
Deep node ranking for neuro-symbolic structural node embedding and classification. Int. J. Intell. Syst. 37(1): 914-943 (2022) - [j95]Sebastian Meznar, Matej Bevec, Nada Lavrac, Blaz Skrlj:
Ontology Completion with Graph-Based Machine Learning: A Comprehensive Evaluation. Mach. Learn. Knowl. Extr. 4(4): 1107-1123 (2022) - [j94]Blaz Skrlj, Saso Dzeroski, Nada Lavrac, Matej Petkovic:
ReliefE: feature ranking in high-dimensional spaces via manifold embeddings. Mach. Learn. 111(1): 273-317 (2022) - 2021
- [j93]Blaz Skrlj, Nika Erzen, Nada Lavrac, Tanja Kunej, Janez Konc:
CaNDis: a web server for investigation of causal relationships between diseases, drugs and drug targets. Bioinform. 37(6): 885-887 (2021) - [j92]Sebastian Meznar, Nada Lavrac, Blaz Skrlj:
Transfer Learning for Node Regression Applied to Spreading Prediction. Complex Syst. 30(4): 457-481 (2021) - [j91]Blaz Skrlj, Matej Martinc, Jan Kralj, Nada Lavrac, Senja Pollak:
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification. Comput. Speech Lang. 65: 101104 (2021) - [j90]Blaz Skrlj, Enja Kokalj, Nada Lavrac:
PubMed-Scale Chemical Concept Embeddings Reconstruct Physical Protein Interaction Networks. Frontiers Res. Metrics Anal. 6: 644614 (2021) - [j89]Blaz Skrlj, Matej Martinc, Nada Lavrac, Senja Pollak:
autoBOT: evolving neuro-symbolic representations for explainable low resource text classification. Mach. Learn. 110(5): 989-1028 (2021) - [c129]Anita Valmarska, Nada Lavrac, Marko Robnik-Sikonja:
Stratification of Parkinson's Disease Patients via Multi-view Clustering. AIME 2021: 229-239 - [c128]Blaz Skrlj, Marko Jukic, Nika Erzen, Senja Pollak, Nada Lavrac:
Prioritization of COVID-19-Related Literature via Unsupervised Keyphrase Extraction and Document Representation Learning. DS 2021: 204-217 - [c127]Enja Kokalj, Blaz Skrlj, Nada Lavrac, Senja Pollak, Marko Robnik-Sikonja:
BERT meets Shapley: Extending SHAP Explanations to Transformer-based Classifiers. EACL (Hackashop) 2021: 16-21 - [c126]Senja Pollak, Marko Robnik-Sikonja, Matthew Purver, Michele Boggia, Ravi Shekhar, Marko Pranjic, Salla Salmela, Ivar Krustok, Tarmo Paju, Carl-Gustav Linden, Leo Leppänen, Elaine Zosa, Matej Ulcar, Linda Freienthal, Silver Traat, Luis Adrián Cabrera-Diego, Matej Martinc, Nada Lavrac, Blaz Skrlj, Martin Znidarsic, Andraz Pelicon, Boshko Koloski, Vid Podpecan, Janez Kranjc, Shane Sheehan, Emanuela Boros, José G. Moreno, Antoine Doucet, Hannu Toivonen:
EMBEDDIA Tools, Datasets and Challenges: Resources and Hackathon Contributions. EACL (Hackashop) 2021: 99-109 - [c125]Senja Pollak, Vid Podpecan, Janez Kranjc, Borut Lesjak, Nada Lavrac:
Scientific Question Generation: Pattern-Based and Graph-Based RoboCHAIR Methods. ICCC 2021: 140-148 - [i16]Blaz Skrlj, Saso Dzeroski, Nada Lavrac, Matej Petkovic:
ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings. CoRR abs/2101.09577 (2021) - [i15]Sebastian Meznar, Nada Lavrac, Blaz Skrlj:
Transfer Learning for Node Regression Applied to Spreading Prediction. CoRR abs/2104.00088 (2021) - [i14]Timen Stepisnik Perdih, Nada Lavrac, Blaz Skrlj:
Semantic Reasoning from Model-Agnostic Explanations. CoRR abs/2106.15433 (2021) - [i13]Blaz Skrlj, Marko Jukic, Nika Erzen, Senja Pollak, Nada Lavrac:
Prioritization of COVID-19-related literature via unsupervised keyphrase extraction and document representation learning. CoRR abs/2110.08874 (2021) - [i12]Sebastian Meznar, Matej Bevec, Nada Lavrac, Blaz Skrlj:
Link Analysis meets Ontologies: Are Embeddings the Answer? CoRR abs/2111.11710 (2021) - 2020
- [j88]Sebastian Meznar, Nada Lavrac, Blaz Skrlj:
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations. IEEE Access 8: 212568-212588 (2020) - [j87]Dominik Kozjek, Rok Vrabic, Borut Rihtarsic, Nada Lavrac, Peter Butala:
Advancing manufacturing systems with big-data analytics: A conceptual framework. Int. J. Comput. Integr. Manuf. 33(2): 169-188 (2020) - [j86]Nada Lavrac, Blaz Skrlj, Marko Robnik-Sikonja:
Propositionalization and embeddings: two sides of the same coin. Mach. Learn. 109(7): 1465-1507 (2020) - [j85]Blaz Skrlj, Jan Kralj, Nada Lavrac:
Embedding-based Silhouette community detection. Mach. Learn. 109(11): 2161-2193 (2020) - [j84]Nada Lavrac, Matej Martinc, Senja Pollak, Marusa Pompe-Novak, Bojan Cestnik:
Bisociative Literature-Based Discovery: Lessons Learned and New Word Embedding Approach. New Gener. Comput. 38(4): 773-800 (2020) - [c124]Anita Valmarska, Dragana Miljkovic, Nada Lavrac, Marko Robnik-Sikonja:
Multi-view Clustering with mvReliefF for Parkinson's Disease Patients Subgroup Detection. AIME 2020: 287-298 - [c123]Sebastian Meznar, Nada Lavrac, Blaz Skrlj:
Prediction of the Effects of Epidemic Spreading with Graph Neural Networks. COMPLEX NETWORKS (1) 2020: 420-431 - [c122]Matej Martinc, Blaz Skrlj, Sergej Pirkmajer, Nada Lavrac, Bojan Cestnik, Martin Marzidovsek, Senja Pollak:
COVID-19 Therapy Target Discovery with Context-Aware Literature Mining. DS 2020: 109-123 - [c121]Blaz Skrlj, Saso Dzeroski, Nada Lavrac, Matej Petkovic:
Feature Importance Estimation with Self-Attention Networks. ECAI 2020: 1491-1498 - [c120]Nada Lavrac, Matej Martinc, Senja Pollak, Bojan Cestnik:
Bisociative Literature-Based Discovery: Lessons Learned and New Prospects. ICCC 2020: 139-145 - [i11]Blaz Skrlj, Saso Dzeroski, Nada Lavrac, Matej Petkovic:
Feature Importance Estimation with Self-Attention Networks. CoRR abs/2002.04464 (2020) - [i10]Nada Lavrac, Blaz Skrlj, Marko Robnik-Sikonja:
Propositionalization and Embeddings: Two Sides of the Same Coin. CoRR abs/2006.04410 (2020) - [i9]Matej Martinc, Blaz Skrlj, Sergej Pirkmajer, Nada Lavrac, Bojan Cestnik, Martin Marzidovsek, Senja Pollak:
COVID-19 therapy target discovery with context-aware literature mining. CoRR abs/2007.15681 (2020) - [i8]Sebastian Meznar, Nada Lavrac, Blaz Skrlj:
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations. CoRR abs/2009.04535 (2020)
2010 – 2019
- 2019
- [j83]Blaz Skrlj, Jan Kralj, Nada Lavrac:
Py3plex toolkit for visualization and analysis of multilayer networks. Appl. Netw. Sci. 4(1): 94:1-94:24 (2019) - [j82]Vid Podpecan, Ziva Ramsak, Kristina Gruden, Hannu Toivonen, Nada Lavrac:
Interactive exploration of heterogeneous biological networks with Biomine Explorer. Bioinform. 35(24): 5385-5388 (2019) - [j81]Ping Xiao, Hannu Toivonen, Oskar Gross, Amílcar Cardoso, João Correia, Penousal Machado, Pedro Martins, Hugo Gonçalo Oliveira, Rahul Sharma, Alexandre Miguel Pinto, Alberto Díaz, Virginia Francisco, Pablo Gervás, Raquel Hervás, Carlos León, Jamie Forth, Matthew Purver, Geraint A. Wiggins, Dragana Miljkovic, Vid Podpecan, Senja Pollak, Jan Kralj, Martin Znidarsic, Marko Bohanec, Nada Lavrac, Tanja Urbancic, Frank van der Velde, Stuart Adam Battersby:
Conceptual Representations for Computational Concept Creation. ACM Comput. Surv. 52(1): 9:1-9:33 (2019) - [j80]Pedro Martins, Hugo Gonçalo Oliveira, João Gonçalves, António Cruz, Amílcar Cardoso, Martin Znidarsic, Nada Lavrac, Simo Linkola, Hannu Toivonen, Raquel Hervás, Gonzalo Méndez, Pablo Gervás:
Computational creativity infrastructure for online software composition: A conceptual blending use case. IBM J. Res. Dev. 63(1): 9:1-9:17 (2019) - [j79]Blaz Skrlj, Jan Kralj, Nada Lavrac:
CBSSD: community-based semantic subgroup discovery. J. Intell. Inf. Syst. 53(2): 265-304 (2019) - [j78]Jan Kralj, Marko Robnik-Sikonja, Nada Lavrac:
NetSDM: Semantic Data Mining with Network Analysis. J. Mach. Learn. Res. 20: 32:1-32:50 (2019) - [j77]Dragan Gamberger, Tjasa Stare, Dragana Miljkovic, Kristina Gruden, Nada Lavrac:
Discovery of Relevant Response in Infected Potato Plants from Time Series of Gene Expression Data. Mach. Learn. Knowl. Extr. 1(1): 400-413 (2019) - [j76]Blaz Skrlj, Jan Kralj, Nada Lavrac, Senja Pollak:
Towards Robust Text Classification with Semantics-Aware Recurrent Neural Architecture. Mach. Learn. Knowl. Extr. 1(2): 575-589 (2019) - [c119]Anita Valmarska, Dragana Miljkovic, Marko Robnik-Sikonja, Nada Lavrac:
Connection Between the Parkinson's Disease Subtypes and Patients' Symptoms Progression. AIME 2019: 263-268 - [c118]Blaz Skrlj, Nada Lavrac, Jan Kralj:
Symbolic Graph Embedding Using Frequent Pattern Mining. DS 2019: 261-275 - [p14]Nada Lavrac, Matjaz Jursic, Borut Sluban, Matic Perovsek, Senja Pollak, Tanja Urbancic, Bojan Cestnik:
Bisociative Knowledge Discovery for Cross-domain Literature Mining. Computational Creativity 2019: 121-139 - [i7]Blaz Skrlj, Matej Martinc, Jan Kralj, Nada Lavrac, Senja Pollak:
tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification. CoRR abs/1902.00438 (2019) - [i6]Blaz Skrlj, Jan Kralj, Janez Konc, Marko Robnik-Sikonja, Nada Lavrac:
Deep Node Ranking: an Algorithm for Structural Network Embedding and End-to-End Classification. CoRR abs/1902.03964 (2019) - [i5]Blaz Skrlj, Jan Kralj, Nada Lavrac:
Embedding-based Silhouette Community Detection. CoRR abs/1908.02556 (2019) - [i4]Blaz Skrlj, Jan Kralj, Nada Lavrac:
Symbolic Graph Embedding using Frequent Pattern Mining. CoRR abs/1910.13314 (2019) - 2018
- [j75]Anita Valmarska, Dragana Miljkovic, Spiros Konitsiotis, Dimitris Gatsios, Nada Lavrac, Marko Robnik-Sikonja:
Symptoms and medications change patterns for Parkinson's disease patients stratification. Artif. Intell. Medicine 91: 82-95 (2018) - [j74]Dragana Miljkovic, Nada Lavrac, Marko Bohanec, Biljana Mileva-Boshkoska:
Discovering dependencies between domains of redox potential and plant defence through triplet extraction and copulas. Int. J. Intell. Eng. Informatics 6(1/2): 61-77 (2018) - [j73]Matej Martinc, Martin Znidarsic, Nada Lavrac, Senja Pollak:
Towards Creative Software Blending: Computational Infrastructure and Use Cases. Informatica (Slovenia) 42(1) (2018) - [j72]Senja Pollak, Geraint A. Wiggins, Martin Znidarsic, Nada Lavrac:
Computational Creativity in Slovenia. Informatica (Slovenia) 42(1) (2018) - [j71]Jan Kralj, Marko Robnik-Sikonja, Nada Lavrac:
HINMINE: heterogeneous information network mining with information retrieval heuristics. J. Intell. Inf. Syst. 50(1): 29-61 (2018) - [j70]Matej Mihelcic, Saso Dzeroski, Nada Lavrac, Tomislav Smuc:
Redescription mining augmented with random forest of multi-target predictive clustering trees. J. Intell. Inf. Syst. 50(1): 63-96 (2018) - [j69]Anita Valmarska, Dragana Miljkovic, Nada Lavrac, Marko Robnik-Sikonja:
Analysis of medications change in Parkinson's disease progression data. J. Intell. Inf. Syst. 51(2): 301-337 (2018) - [j68]Anita Valmarska, Dragana Miljkovic, Nada Lavrac, Marko Robnik-Sikonja:
Correction to: Analysis of medications change in Parkinson's disease progression data. J. Intell. Inf. Syst. 51(2): 339-340 (2018) - [c117]Blaz Skrlj, Jan Kralj, Nada Lavrac:
Py3plex: A Library for Scalable Multilayer Network Analysis and Visualization. COMPLEX NETWORKS (1) 2018: 757-768 - [c116]Anita Valmarska, Dragana Miljkovic, Marko Robnik-Sikonja, Nada Lavrac:
Visualization and Analysis of Parkinson's Disease Status and Therapy Patterns. DS 2018: 466-479 - [c115]Geraint A. Wiggins, Nada Lavrac, Vid Podpecan, Senja Pollak:
Conceptualising Computational Creativity: Towards automated historiography of a research field. ICCC 2018: 288-295 - [c114]Blaz Skrlj, Jan Kralj, Nada Lavrac:
Targeted End-to-End Knowledge Graph Decomposition. ILP 2018: 157-171 - 2017
- [j67]Matej Mihelcic, Saso Dzeroski, Nada Lavrac, Tomislav Smuc:
A framework for redescription set construction. Expert Syst. Appl. 68: 196-215 (2017) - [j66]Anita Valmarska, Nada Lavrac, Johannes Fürnkranz, Marko Robnik-Sikonja:
Refinement and selection heuristics in subgroup discovery and classification rule learning. Expert Syst. Appl. 81: 147-162 (2017) - [j65]Donatella Gubiani, Elsa Fabbretti, Bojan Cestnik, Nada Lavrac, Tanja Urbancic:
Outlier based literature exploration for cross-domain linking of Alzheimer's disease and gut microbiota. Expert Syst. Appl. 85: 386-396 (2017) - [j64]Janez Kranjc, Roman Orac, Vid Podpecan, Nada Lavrac, Marko Robnik-Sikonja:
ClowdFlows: Online workflows for distributed big data mining. Future Gener. Comput. Syst. 68: 38-58 (2017) - [c113]Anita Valmarska, Dragana Miljkovic, Spiros Konitsiotis, Dimitris Gatsios, Nada Lavrac, Marko Robnik-Sikonja:
Combining Multitask Learning and Short Time Series Analysis in Parkinson's Disease Patients Stratification. AIME 2017: 116-125 - [c112]Blaz Skrlj, Jan Kralj, Anze Vavpetic, Nada Lavrac:
Community-Based Semantic Subgroup Discovery. NFMCP@PKDD/ECML 2017: 182-196 - [r2]Petra Kralj Novak, Nada Lavrac, Geoffrey I. Webb:
Supervised Descriptive Rule Induction. Encyclopedia of Machine Learning and Data Mining 2017: 1210-1213 - [i3]Matej Mihelcic, Goran Simic, Mirjana Babic Leko, Nada Lavrac, Saso Dzeroski, Tomislav Smuc:
Using Redescription Mining to Relate Clinical and Biological Characteristics of Cognitively Impaired and Alzheimer's Disease Patients. CoRR abs/1702.06831 (2017) - 2016
- [j63]Dragan Gamberger, Bernard Zenko, Alexis Mitelpunkt, Netta Shachar, Nada Lavrac:
Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Brain Informatics 3(3): 169-179 (2016) - [j62]Prem Raj Adhikari, Anze Vavpetic, Jan Kralj, Nada Lavrac, Jaakko Hollmén:
Explaining mixture models through semantic pattern mining and banded matrix visualization. Mach. Learn. 105(1): 3-39 (2016) - [j61]Matic Perovsek, Janez Kranjc, Tomaz Erjavec, Bojan Cestnik, Nada Lavrac:
TextFlows: A visual programming platform for text mining and natural language processing. Sci. Comput. Program. 121: 128-152 (2016) - [c111]Dragana Miljkovic, Nada Lavrac, Marko Bohanec, Biljana Mileva-Boshkoska:
Detection of dependencies between literature domains through relation extraction and copulas. CoDIT 2016: 302-307 - [c110]Senja Pollak, Biljana Mileva-Boshkoska, Dragana Miljkovic, Geraint A. Wiggins, Nada Lavrac:
Computational Creativity Conceptualisation Grounded on ICCC Papers. ICCC 2016: 123-130 - [c109]Martin Znidarsic, Amílcar Cardoso, Pablo Gervás, Pedro Martins, Raquel Hervás, Ana Oliveira Alves, Hugo Gonçalo Oliveira, Ping Xiao, Simo Linkola, Hannu Toivonen, Janez Kranjc, Nada Lavrac:
Computational Creativity Infrastructure for Online Software Composition: A Conceptual Blending Use Case. ICCC 2016: 371-379 - [c108]Jasmina Smailovic, Miha Grcar, Nada Lavrac, Martin Znidarsic:
Dynamic Parameter Adaptation of SVM Based Active Learning Methodology. AL@iKNOW 2016: 17-24 - [c107]Jan Kralj, Anze Vavpetic, Michel Dumontier, Nada Lavrac:
Network Ranking Assisted Semantic Data Mining. IWBBIO 2016: 752-764 - [c106]Anita Valmarska, Dragana Miljkovic, Marko Robnik-Sikonja, Nada Lavrac:
Multi-view Approach to Parkinson's Disease Quality of Life Data Analysis. NFMCP@PKDD/ECML 2016: 163-178 - [c105]Tadej Skvorc, Nada Lavrac, Marko Robnik-Sikonja:
Co-Bidding Graphs for Constrained Paper Clustering. SLATE 2016: 1:1-1:13 - [p13]Matic Perovsek, Matjaz Jursic, Bojan Cestnik, Nada Lavrac:
Empowering Bridging Term Discovery for Cross-Domain Literature Mining in the TextFlows Platform. Machine Learning for Health Informatics 2016: 59-98 - [p12]Dragana Miljkovic, Darko Aleksovski, Vid Podpecan, Nada Lavrac, Bernd Malle, Andreas Holzinger:
Machine Learning and Data Mining Methods for Managing Parkinson's Disease. Machine Learning for Health Informatics 2016: 209-220 - [i2]Matej Mihelcic, Saso Dzeroski, Nada Lavrac, Tomislav Smuc:
A framework for redescription set construction. CoRR abs/1606.03935 (2016) - 2015
- [j60]Matic Perovsek, Anze Vavpetic, Janez Kranjc, Bojan Cestnik, Nada Lavrac:
Wordification: Propositionalization by unfolding relational data into bags of words. Expert Syst. Appl. 42(17-18): 6442-6456 (2015) - [j59]Borut Sluban, Nada Lavrac:
Relating ensemble diversity and performance: A study in class noise detection. Neurocomputing 160: 120-131 (2015) - [j58]Janez Kranjc, Jasmina Smailovic, Vid Podpecan, Miha Grcar, Martin Znidarsic, Nada Lavrac:
Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform. Inf. Process. Manag. 51(2): 187-203 (2015) - [c104]Dragan Gamberger, Bernard Zenko, Alexis Mitelpunkt, Nada Lavrac:
Identification of Gender Specific Biomarkers for Alzheimer's Disease. BIH 2015: 57-66 - [c103]Pedro Martins, Tanja Urbancic, Senja Pollak, Nada Lavrac, Amílcar Cardoso:
The Good, the Bad, and the AHA! Blends. ICCC 2015: 166-173 - [c102]Dragan Gamberger, Bernard Zenko, Alexis Mitelpunkt, Nada Lavrac:
Multilayer Clustering: Biomarker Driven Segmentation of Alzheimer's Disease Patient Population. IWBBIO (1) 2015: 134-145 - [c101]Nada Lavrac, Anze Vavpetic:
Relational and Semantic Data Mining - - Invited Talk -. LPNMR 2015: 20-31 - [c100]Jan Kralj, Anita Valmarska, Marko Robnik-Sikonja, Nada Lavrac:
Mining Text Enriched Heterogeneous Citation Networks. PAKDD (1) 2015: 672-683 - [c99]Matej Mihelcic, Saso Dzeroski, Nada Lavrac, Tomislav Smuc:
Redescription Mining with Multi-target Predictive Clustering Trees. NFMCP 2015: 125-143 - [c98]Jan Kralj, Marko Robnik-Sikonja, Nada Lavrac:
Heterogeneous Network Decomposition and Weighting with Text Mining Heuristics. NFMCP 2015: 194-208 - [c97]Senja Pollak, Borut Lesjak, Janez Kranjc, Vid Podpecan, Martin nidaric, Nada Lavrac:
RoboCHAIR: Creative Assistant for Question Generation and Ranking. SSCI 2015: 1468-1475 - 2014
- [j57]Dany Morisset, Petra Kralj Novak, Darko Zupanic, Kristina Gruden, Nada Lavrac, Jana Zel:
GMOseek: a user friendly tool for optimized GMO testing. BMC Bioinform. 15: 258 (2014) - [j56]Borut Sluban, Dragan Gamberger, Nada Lavrac:
Ensemble-based noise detection: noise ranking and visual performance evaluation. Data Min. Knowl. Discov. 28(2): 265-303 (2014) - [j55]Dragana Miljkovic, Matjaz Depolli, Tjasa Stare, Igor Mozetic, Marko Petek, Kristina Gruden, Nada Lavrac:
Plant defence model revisions through iterative minimisation of constraint violations. Int. J. Comput. Biol. Drug Des. 7(1): 61-79 (2014) - [j54]Jasmina Smailovic, Miha Grcar, Nada Lavrac, Martin Znidarsic:
Stream-based active learning for sentiment analysis in the financial domain. Inf. Sci. 285: 181-203 (2014) - [j53]Anze Vavpetic, Vid Podpecan, Nada Lavrac:
Semantic subgroup explanations. J. Intell. Inf. Syst. 42(2): 233-254 (2014) - [c96]Prem Raj Adhikari, Anze Vavpetic, Jan Kralj, Nada Lavrac, Jaakko Hollmén:
Explaining Mixture Models through Semantic Pattern Mining and Banded Matrix Visualization. Discovery Science 2014: 1-12 - [c95]Dragan Gamberger, Matej Mihelcic, Nada Lavrac:
Multilayer Clustering: A Discovery Experiment on Country Level Trading Data. Discovery Science 2014: 87-98 - [c94]Maria Teresa Llano, Rose Hepworth, Simon Colton, Jeremy Gow, John William Charnley, Nada Lavrac, Martin Znidarsic, Matic Perovsek, Mark Granroth-Wilding, Stephen Clark:
Baseline Methods for Automated Fictional Ideation. ICCC 2014: 211-219 - [c93]Nada Lavrac, Matic Perovsek, Anze Vavpetic:
Propositionalization Online. ECML/PKDD (3) 2014: 456-459 - [e10]Simon Colton, Dan Ventura, Nada Lavrac, Michael Cook:
Proceedings of the Fifth International Conference on Computational Creativity, ICCC 2014, Ljubljana, Slovenia, June 10-13, 2014. computationalcreativity.net 2014, ISBN 978-961-264-055-2 [contents] - 2013
- [j52]Laura Langohr, Vid Podpecan, Marko Petek, Igor Mozetic, Kristina Gruden, Nada Lavrac, Hannu Toivonen:
Contrasting Subgroup Discovery. Comput. J. 56(3): 289-303 (2013) - [j51]Anze Vavpetic, Nada Lavrac:
Semantic Subgroup Discovery Systems and Workflows in the SDM-Toolkit. Comput. J. 56(3): 304-320 (2013) - [j50]Miha Grcar, Nejc Trdin, Nada Lavrac:
A Methodology for Mining Document-Enriched Heterogeneous Information Networks. Comput. J. 56(3): 321-335 (2013) - [j49]Nada Lavrac, Petra Kralj Novak:
Relational and Semantic Data Mining for Biomedical Research. Informatica (Slovenia) 37(1): 35-39 (2013) - [c92]Vid Podpecan, Dragana Miljkovic, Marko Petek, Tjasa Stare, Kristina Gruden, Igor Mozetic, Nada Lavrac:
Integrating semantic transcriptomic data analysis and knowledge extraction from biological literature. BIBM 2013: 477-480 - [c91]Janez Kranjc, Vid Podpecan, Nada Lavrac:
Real-time data analysis in ClowdFlows. IEEE BigData 2013: 15-22 - [c90]Matic Perovsek, Anze Vavpetic, Bojan Cestnik, Nada Lavrac:
A Wordification Approach to Relational Data Mining. Discovery Science 2013: 141-154 - [c89]Anze Vavpetic, Petra Kralj Novak, Miha Grcar, Igor Mozetic, Nada Lavrac:
Semantic Data Mining of Financial News Articles. Discovery Science 2013: 294-307 - [c88]Matic Perovsek, Bojan Cestnik, Tanja Urbancic, Simon Colton, Nada Lavrac:
Towards Narrative Ideation via Cross-Context Link Discovery Using Banded Matrices. IDA 2013: 333-344 - [c87]Dragana Miljkovic, Vid Podpecan, Tjasa Stare, Igor Mozetic, Kristina Gruden, Nada Lavrac:
Incremental revision of biological networks from texts. IWBBIO 2013: 1-9 - [c86]Borut Sluban, Nada Lavrac:
ViperCharts: Visual Performance Evaluation Platform. ECML/PKDD (3) 2013: 650-653 - [c85]Jasmina Smailovic, Miha Grcar, Nada Lavrac, Martin Znidarsic:
Predictive Sentiment Analysis of Tweets: A Stock Market Application. CHI-KDD 2013: 77-88 - [c84]Matjaz Jursic, Bojan Cestnik, Tanja Urbancic, Nada Lavrac:
HCI Empowered Literature Mining for Cross-Domain Knowledge Discovery. CHI-KDD 2013: 124-135 - 2012
- [b3]Johannes Fürnkranz, Dragan Gamberger, Nada Lavrac:
Foundations of Rule Learning. Cognitive Technologies, Springer 2012, ISBN 978-3-540-75196-0, pp. 1-298 - [j48]Ingrid Petric, Bojan Cestnik, Nada Lavrac, Tanja Urbancic:
Outlier Detection in Cross-Context Link Discovery for Creative Literature Mining. Comput. J. 55(1): 47-61 (2012) - [j47]Vid Podpecan, Monika Zemenova, Nada Lavrac:
Orange4WS Environment for Service-Oriented Data Mining. Comput. J. 55(1): 82-98 (2012) - [c83]Dragana Miljkovic, Matjaz Depolli, Igor Mozetic, Nada Lavrac, Tjasa Stare, Marko Petek, Kristina Gruden:
Constraint-driven optimization of plant defense model parameters. BIBM Workshops 2012: 570-574 - [c82]Nada Lavrac:
Advances in Data Mining for Biomedical Research. CBMS 2012: 1-5 - [c81]Matjaz Jursic, Bojan Cestnik, Tanja Urbancic, Nada Lavrac:
Cross-domain literature mining: Finding bridging concepts with CrossBee. ICCC 2012: 33-40 - [c80]Matic Perovsek, Anze Vavpetic, Nada Lavrac:
A Wordification Approach to Relational Data Mining: Early Results. ILP (Late Breaking Papers) 2012: 56-61 - [c79]Senja Pollak, Anze Vavpetic, Janez Kranjc, Nada Lavrac, Spela Vintar:
NLP workflow for on-line definition extraction from English and Slovene text corpora. KONVENS 2012: 53-60 - [c78]Borut Sluban, Senja Pollak, Roel Coesemans, Nada Lavrac:
Irregularity Detection in Categorized Document Corpora. LREC 2012: 1598-1603 - [c77]Janez Kranjc, Vid Podpecan, Nada Lavrac:
ClowdFlows: A Cloud Based Scientific Workflow Platform. ECML/PKDD (2) 2012: 816-819 - [c76]Anze Vavpetic, Vid Podpecan, Stijn Meganck, Nada Lavrac:
Explaining Subgroups through Ontologies. PRICAI 2012: 625-636 - [p11]Matjaz Jursic, Borut Sluban, Bojan Cestnik, Miha Grcar, Nada Lavrac:
Bridging Concept Identification for Constructing Information Networks from Text Documents. Bisociative Knowledge Discovery 2012: 66-90 - [p10]Ingrid Petric, Bojan Cestnik, Nada Lavrac, Tanja Urbancic:
Bisociative Knowledge Discovery by Literature Outlier Detection. Bisociative Knowledge Discovery 2012: 313-324 - [p9]Borut Sluban, Matjaz Jursic, Bojan Cestnik, Nada Lavrac:
Exploring the Power of Outliers for Cross-Domain Literature Mining. Bisociative Knowledge Discovery 2012: 325-337 - [p8]Matjaz Jursic, Bojan Cestnik, Tanja Urbancic, Nada Lavrac:
Bisociative Literature Mining by Ensemble Heuristics. Bisociative Knowledge Discovery 2012: 338-358 - [p7]Igor Mozetic, Nada Lavrac:
Applications and Evaluation: Overview. Bisociative Knowledge Discovery 2012: 359-363 - [p6]Igor Mozetic, Nada Lavrac, Vid Podpecan, Petra Kralj Novak, Helena Motaln, Marko Petek, Kristina Gruden, Hannu Toivonen, Kimmo Kulovesi:
Semantic Subgroup Discovery and Cross-Context Linking for Microarray Data Analysis. Bisociative Knowledge Discovery 2012: 379-389 - [p5]Dragana Miljkovic, Vid Podpecan, Miha Grcar, Kristina Gruden, Tjasa Stare, Marko Petek, Igor Mozetic, Nada Lavrac:
Modelling a Biological System: Network Creation by Triplet Extraction from Biological Literature. Bisociative Knowledge Discovery 2012: 427-437 - 2011
- [j46]Vid Podpecan, Nada Lavrac, Igor Mozetic, Petra Kralj Novak, Igor Trajkovski, Laura Langohr, Kimmo Kulovesi, Hannu Toivonen, Marko Petek, Helena Motaln, Kristina Gruden:
SegMine workflows for semantic microarray data analysis in Orange4WS. BMC Bioinform. 12: 416 (2011) - [j45]Monika Záková, Petr Kremen, Filip Zelezný, Nada Lavrac:
Automating Knowledge Discovery Workflow Composition Through Ontology-Based Planning. IEEE Trans Autom. Sci. Eng. 8(2): 253-264 (2011) - [c75]Borut Sluban, Matjaz Jursic, Bojan Cestnik, Nada Lavrac:
Evaluating Outliers for Cross-Context Link Discovery. AIME 2011: 343-347 - [c74]Miha Grcar, Nada Lavrac:
A Methodology for Mining Document-Enriched Heterogeneous Information Networks. Discovery Science 2011: 107-121 - [c73]Nada Lavrac, Anze Vavpetic, Larisa N. Soldatova, Igor Trajkovski, Petra Kralj Novak:
Using Ontologies in Semantic Data Mining with SEGS and g-SEGS. Discovery Science 2011: 165-178 - [c72]Anze Vavpetic, Nada Lavrac:
Using Ontologies in Semantic Data Mining with g-SEGS and Aleph. ILP (Late Breaking Papers) 2011: 19-26 - [e9]Mor Peleg, Nada Lavrac, Carlo Combi:
Artificial Intelligence in Medicine - 13th Conference on Artificial Intelligence in Medicine, AIME 2011, Bled, Slovenia, July 2-6, 2011. Proceedings. Lecture Notes in Computer Science 6747, Springer 2011, ISBN 978-3-642-22217-7 [contents] - [i1]Dragan Gamberger, Nada Lavrac:
Expert-Guided Subgroup Discovery: Methodology and Application. CoRR abs/1106.4576 (2011) - 2010
- [j44]Borut Sluban, Nada Lavrac:
Supporting the search for cross-context links by outlier detection methods. BMC Bioinform. 11(S-5): P2 (2010) - [j43]Matjaz Jursic, Igor Mozetic, Miha Grcar, Bojan Cestnik, Nada Lavrac:
Identification of concepts bridging diverse biomedical domains. BMC Bioinform. 11(S-5): P4 (2010) - [j42]Matjaz Jursic, Igor Mozetic, Tomaz Erjavec, Nada Lavrac:
LemmaGen: Multilingual Lemmatisation with Induced Ripple-Down Rules. J. Univers. Comput. Sci. 16(9): 1190-1214 (2010) - [c71]Miha Grcar, Vid Podpecan, Matjaz Jursic, Nada Lavrac:
Efficient Visualization of Document Streams. Discovery Science 2010: 174-188 - [c70]Borut Sluban, Dragan Gamberger, Nada Lavrac:
Advances in Class Noise Detection. ECAI 2010: 1105-1106 - [c69]Igor Mozetic, Nada Lavrac, Vid Podpecan, Petra Kralj Novak, Helena Motaln, Marko Petek, Kristina Gruden, Hannu Toivonen, Kimmo Kulovesi:
Bisociative Knowledge Discovery for Microarray Data Analysis. ICCC 2010: 190-199 - [c68]Vid Podpecan, Monika Záková, Nada Lavrac:
Workflow Construction for Service-Oriented Knowledge Discovery. ISoLA (1) 2010: 313-327 - [c67]Vid Podpecan, Miha Grcar, Nada Lavrac:
Semi-supervised Constrained Clustering: An Expert-Guided Data Analysis Methodology. PRICAI 2010: 219-230 - [c66]Borut Sluban, Dragan Gamberger, Nada Lavrac:
Performance Analysis of Class Noise Detection Algorithms. STAIRS 2010: 303-314 - [p4]Nada Lavrac, Blaz Zupan:
Data Mining in Medicine. Data Mining and Knowledge Discovery Handbook 2010: 1111-1136 - [p3]Nada Lavrac, Miha Grcar, Blaz Fortuna, Paola Velardi:
Exploratory Analysis of the Social Network of Researchers in Inductive Logic Programming. Computational Social Network Analysis 2010: 135-154 - [p2]Nada Lavrac, Johannes Fürnkranz, Dragan Gamberger:
Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms. Advances in Machine Learning I 2010: 121-146 - [r1]Petra Kralj Novak, Nada Lavrac, Geoffrey I. Webb:
Supervised Descriptive Rule Induction. Encyclopedia of Machine Learning 2010: 938-941
2000 – 2009
- 2009
- [j41]Petra Kralj Novak, Nada Lavrac, Dragan Gamberger, Antonija Krstacic:
CSM-SD: Methodology for contrast set mining through subgroup discovery. J. Biomed. Informatics 42(1): 113-122 (2009) - [j40]Petra Kralj Novak, Nada Lavrac, Geoffrey I. Webb:
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining. J. Mach. Learn. Res. 10: 377-403 (2009) - [j39]Filip Zelezný, Nada Lavrac:
Guest editors' introduction: Special issue on Inductive Logic Programming (ILP-2008). Mach. Learn. 76(1): 1-2 (2009) - 2008
- [j38]Joël Plisson, Nada Lavrac, Dunja Mladenic, Tomaz Erjavec:
Ripple Down Rule learning for automated word lemmatisation. AI Commun. 21(1): 15-26 (2008) - [j37]Igor Trajkovski, Nada Lavrac, Jakub Tolar:
SEGS: Search for enriched gene sets in microarray data. J. Biomed. Informatics 41(4): 588-601 (2008) - [j36]Gemma C. Garriga, Petra Kralj, Nada Lavrac:
Closed Sets for Labeled Data. J. Mach. Learn. Res. 9: 559-580 (2008) - [j35]Igor Trajkovski, Filip Zelezný, Nada Lavrac, Jakub Tolar:
Learning Relational Descriptions of Differentially Expressed Gene Groups. IEEE Trans. Syst. Man Cybern. Part C 38(1): 16-25 (2008) - [c65]Jeroen S. de Bruin, Joost N. Kok, Nada Lavrac, Igor Trajkovski:
On the Design of Knowledge Discovery Services Design Patterns and Their Application in a Use Case Implementation. ISoLA 2008: 649-662 - [c64]Blaz Fortuna, Nada Lavrac, Paola Velardi:
Advancing Topic Ontology Learning through Term Extraction. PRICAI 2008: 626-635 - [c63]Dragan Gamberger, Nada Lavrac, Johannes Fürnkranz:
Handling Unknown and Imprecise Attribute Values in Propositional Rule Learning: A Feature-Based Approach. PRICAI 2008: 636-645 - [e8]Filip Zelezný, Nada Lavrac:
Inductive Logic Programming, 18th International Conference, ILP 2008, Prague, Czech Republic, September 10-12, 2008, Proceedings. Lecture Notes in Computer Science 5194, Springer 2008, ISBN 978-3-540-85927-7 [contents] - 2007
- [j34]Dragan Gamberger, Nada Lavrac, Antonija Krstacic, Goran Krstacic:
Clinical data analysis based on iterative subgroup discovery: experiments in brain ischaemia data analysis. Appl. Intell. 27(3): 205-217 (2007) - [j33]Nada Lavrac, Marko Bohanec, Aleksander Pur, Bojan Cestnik, Marko Debeljak, Andrej Kobler:
Data mining and visualization for decision support and modeling of public health-care resources. J. Biomed. Informatics 40(4): 438-447 (2007) - [j32]Nada Lavrac, Peter Ljubic, Tanja Urbani, Gregor Papa, Mitja Jermol, Stefan Bollhalter:
Trust Modeling for Networked Organizations Using Reputation and Collaboration Estimates. IEEE Trans. Syst. Man Cybern. Part C 37(3): 429-439 (2007) - [j31]Joël Plisson, Peter Ljubic, Igor Mozetic, Nada Lavrac:
An Ontology for Virtual Organization Breeding Environments. IEEE Trans. Syst. Man Cybern. Part C 37(6): 1327-1341 (2007) - [c62]Petra Kralj, Nada Lavrac, Dragan Gamberger, Antonija Krstacic:
Contrast Set Mining for Distinguishing Between Similar Diseases. AIME 2007: 109-118 - [c61]Igor Trajkovski, Nada Lavrac:
Interpreting Gene Expression Data by Searching for Enriched Gene Sets. AIME 2007: 144-148 - [c60]Dragan Gamberger, Nada Lavrac:
Supporting Factors in Descriptive Analysis of Brain Ischaemia. AIME 2007: 155-159 - [c59]Aleksander Pur, Marko Bohanec, Nada Lavrac, Bojan Cestnik, Marko Debeljak, Anton Gradisek:
Monitoring Human Resources of a Public Health-Care System Through Intelligent Data Analysis and Visualization. AIME 2007: 175-179 - [c58]Damjan Demsar, Igor Mozetic, Nada Lavrac:
Collaboration Opportunity Finder. Virtual Enterprises and Collaborative Networks 2007: 179-186 - [c57]Igor Trajkovski, Nada Lavrac:
Efficient Generation of Biologically Relevant Enriched Gene Sets. ISBRA 2007: 248-259 - [c56]Petra Kralj, Nada Lavrac, Dragan Gamberger, Antonija Krstacic:
Contrast Set Mining Through Subgroup Discovery Applied to Brain Ischaemina Data. PAKDD 2007: 579-586 - [e7]Michael R. Berthold, John Shawe-Taylor, Nada Lavrac:
Advances in Intelligent Data Analysis VII, 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, Proceedings. Lecture Notes in Computer Science 4723, Springer 2007, ISBN 978-3-540-74824-3 [contents] - 2006
- [j30]Branko Kavsek, Nada Lavrac:
APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery. Appl. Artif. Intell. 20(7): 543-583 (2006) - [j29]Filip Zelezný, Nada Lavrac:
Propositionalization-based relational subgroup discovery with RSD. Mach. Learn. 62(1-2): 33-63 (2006) - [c55]Igor Trajkovski, Filip Zelezný, Jakub Tolar, Nada Lavrac:
Relational Subgroup Discovery for Descriptive Analysis of Microarray Data. CompLife 2006: 86-96 - [c54]Tanja Urbancic, Olga Stepánková, Nada Lavrac:
Enhancing Human Choice by Information Technologies. HCC 2006: 255-264 - [c53]Monika Záková, Filip Zelezný, Javier A. García-Sedano, Cyril Masia Tissot, Nada Lavrac, Petr Kremen, Javier Molina:
Relational Data Mining Applied to Virtual Engineering of Product Designs. ILP 2006: 439-453 - [c52]Gemma C. Garriga, Petra Kralj, Nada Lavrac:
Closed Sets for Labeled Data. PKDD 2006: 163-174 - [c51]Igor Trajkovski, Filip Zelezný, Nada Lavrac, Jakub Tolar:
Relational Descriptive Analysis of Gene Expression Data. STAIRS 2006: 184-195 - [e6]Ljupco Todorovski, Nada Lavrac, Klaus P. Jantke:
Discovery Science, 9th International Conference, DS 2006, Barcelona, Spain, October 7-10, 2006, Proceedings. Lecture Notes in Computer Science 4265, Springer 2006, ISBN 3-540-46491-3 [contents] - 2005
- [c50]Nada Lavrac, Marko Bohanec, Aleksander Pur, Bojan Cestnik, Mitja Jermol, Tanja Urbancic, Marko Debeljak, Branko Kavsek, Tadeja Kopac:
Resource Modeling and Analysis of Regional Public Health Care Data by Means of Knowledge Technologies. AIME 2005: 414-418 - [c49]Nada Lavrac:
SolEuNet: Selected Data Mining Techniques and Applications. GfKl 2005: 32-39 - [c48]Aleksander Pur, Marko Bohanec, Bojan Cestnik, Nada Lavrac, Marko Debeljak, Tadeja Kopac:
Data Mining for Decision Support: An Application in Public Health Care. IEA/AIE 2005: 459-469 - [c47]Nada Lavrac, Peter Ljubic, Mitja Jermol, Gregor Papa:
A Decision Support Approach to Modeling Trust in Networked Organizations. IEA/AIE 2005: 746-748 - [c46]Toni Jarimo, Peter Ljubic, Iiro Salkari, Marko Bohanec, Nada Lavrac, Martin Znidarsic, Stefan Bollhalter, Jirí Hodík:
Hierarchical Multi-Attribute Decision Support Approach to Virtual Organization Creation. PRO-VE 2005: 135-142 - [c45]Nada Lavrac, Peter Ljubic, Mitja Jermol, Stefan Bollhalter:
A Decision Support Approach to Trust Modeling in Networked Organizations. PRO-VE 2005: 167-174 - [c44]Nada Lavrac:
Subgroup Discovery Techniques and Applications. PAKDD 2005: 2-14 - [p1]Nada Lavrac, Blaz Zupan:
Data Mining in Medicine. The Data Mining and Knowledge Discovery Handbook 2005: 1107-1138 - 2004
- [j28]Dragan Gamberger, Nada Lavrac, Filip Zelezný, Jakub Tolar:
Induction of comprehensible models for gene expression datasets by subgroup discovery methodology. J. Biomed. Informatics 37(4): 269-284 (2004) - [j27]Nada Lavrac, Branko Kavsek, Peter A. Flach, Ljupco Todorovski:
Subgroup Discovery with CN2-SD. J. Mach. Learn. Res. 5: 153-188 (2004) - [j26]Nada Lavrac, Hiroshi Motoda, Tom Fawcett:
Editorial: Data Mining Lessons Learned. Mach. Learn. 57(1-2): 5-11 (2004) - [j25]Nada Lavrac, Hiroshi Motoda, Tom Fawcett, Robert Holte, Pat Langley, Pieter W. Adriaans:
Introduction: Lessons Learned from Data Mining Applications and Collaborative Problem Solving. Mach. Learn. 57(1-2): 13-34 (2004) - [j24]Nada Lavrac, Bojan Cestnik, Dragan Gamberger, Peter A. Flach:
Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned. Mach. Learn. 57(1-2): 115-143 (2004) - [c43]Nada Lavrac, Dragan Gamberger:
Relevancy in Constraint-Based Subgroup Discovery. Constraint-Based Mining and Inductive Databases 2004: 243-266 - [c42]Nada Lavrac, Filip Zelezný, Saso Dzeroski:
Local Patterns: Theory and Practice of Constraint-Based Relational Subgroup Discovery. Local Pattern Detection 2004: 71-88 - [c41]Dragan Gamberger, Nada Lavrac:
Avoiding Data Overfitting in Scientific Discovery: Experiments in Functional Genomics. ECAI 2004: 470-474 - [c40]Mitja Jermol, Nada Lavrac, Tanja Urbancic, Tadeja Kopac:
Supporting a Public Health Care Virtual Organization by Knowledge Technologies. Virtual Enterprises and Collaborative Networks 2004: 567-576 - [c39]Branko Kavsek, Nada Lavrac, Ljupco Todorovski:
ROC Analysis of Example Weighting in Subgroup Discovery. ROCAI 2004: 55-60 - 2003
- [j23]Dragan Gamberger, Nada Lavrac:
Active subgroup mining: a case study in coronary heart disease risk group detection. Artif. Intell. Medicine 28(1): 27-57 (2003) - [j22]Mitja Jermol, Nada Lavrac, Tanja Urbancic:
Managing business intelligence in a virtual enterprise: A case study and knowledge management lessons learned. J. Intell. Fuzzy Syst. 14(3): 121-136 (2003) - [c38]Dragan Gamberger, Nada Lavrac:
Analysis of Gene Expression Data by the Logic Minimization Approach. AIME 2003: 244-248 - [c37]Branko Kavsek, Nada Lavrac, Viktor Jovanoski:
APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery. IDA 2003: 230-241 - [c36]Mark-A. Krogel, Simon Alan Rawles, Filip Zelezný, Peter A. Flach, Nada Lavrac, Stefan Wrobel:
Comparative Evaluation of Approaches to Propositionalization. ILP 2003: 197-214 - [e5]Nada Lavrac, Dragan Gamberger, Ljupco Todorovski, Hendrik Blockeel:
Machine Learning: ECML 2003, 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Proceedings. Lecture Notes in Computer Science 2837, Springer 2003, ISBN 3-540-20121-1 [contents] - [e4]Nada Lavrac, Dragan Gamberger, Hendrik Blockeel, Ljupco Todorovski:
Knowledge Discovery in Databases: PKDD 2003, 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, Proceedings. Lecture Notes in Computer Science 2838, Springer 2003, ISBN 3-540-20085-1 [contents] - 2002
- [j21]Dragan Gamberger, Nada Lavrac:
Expert-Guided Subgroup Discovery: Methodology and Application. J. Artif. Intell. Res. 17: 501-527 (2002) - [j20]Dragan Gamberger, Nada Lavrac, Goran Krstacic:
Confirmation rule induction and its applications to coronary heart disease diagnosis and risk group discovery. J. Intell. Fuzzy Syst. 12(1): 35-48 (2002) - [c35]Peter A. Flach, Nada Lavrac:
Learning in Clausal Logic: A Perspective on Inductive Logic Programming. Computational Logic: Logic Programming and Beyond 2002: 437-471 - [c34]Nada Lavrac, Peter A. Flach, Branko Kavsek, Ljupco Todorovski:
Adapting classification rule induction to subgroup discovery. ICDM 2002: 266-273 - [c33]Dragan Gamberger, Nada Lavrac:
Descriptive Induction through Subgroup Discovery: A Case Study in a Medical Domain. ICML 2002: 163-170 - [c32]Nada Lavrac:
Virtual Enterprise for Data Mining and Decision Support. PRO-VE 2002 - [c31]Nada Lavrac, Filip Zelezný, Peter A. Flach:
RSD: Relational Subgroup Discovery through First-Order Feature Construction. ILP 2002: 149-165 - [c30]Dragan Gamberger, Nada Lavrac:
Generating Actionable Knowledge by Expert-Guided Subgroup Discovery. PKDD 2002: 163-174 - 2001
- [j19]Elpida T. Keravnou, Nada Lavrac:
AIM portraits: tracing the evolution of artificial intelligence in medicine and predicting its future in the new millennium. Artif. Intell. Medicine 23(1): 1-4 (2001) - [j18]Nada Lavrac, Peter A. Flach:
An extended transformation approach to inductive logic programming. ACM Trans. Comput. Log. 2(4): 458-494 (2001) - [c29]Branko Kavsek, Nada Lavrac, Anuska Ferligoj:
Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction. ECML 2001: 251-262 - [c28]Viktor Jovanoski, Nada Lavrac:
Classification Rule Learning with APRIORI-C. EPIA 2001: 44-51 - 2000
- [j17]Dragan Gamberger, Nada Lavrac, Saso Dzeroski:
Noise Detection and Elimination in data Proprocessing: Experiments in Medical Domains. Appl. Artif. Intell. 14(2): 205-223 (2000) - [c27]Dragan Gamberger, Nada Lavrac, Goran Krstacic, Tomislav Smuc:
Inconsistency Tests for Patient Records in a Coronary Heart Disease Database. ISMDA 2000: 183-189 - [c26]Dragan Gamberger, Nada Lavrac:
Confirmation Rule Sets. PKDD 2000: 34-43 - [c25]Ljupco Todorovski, Peter A. Flach, Nada Lavrac:
Predictive Performance of Weghted Relative Accuracy. PKDD 2000: 255-264
1990 – 1999
- 1999
- [j16]Nada Lavrac:
Selected techniques for data mining in medicine. Artif. Intell. Medicine 16(1): 3-23 (1999) - [j15]Saso Dzeroski, Nada Lavrac:
Editorial. Data Min. Knowl. Discov. 3(1): 5-6 (1999) - [j14]Nada Lavrac, Dragan Gamberger, Viktor Jovanoski:
A Study of Relevance for Learning in Deductive Databases. J. Log. Program. 40(2-3): 215-249 (1999) - [j13]Nada Lavrac, Saso Dzeroski, Masayuki Numao:
Inductive Logic Programming for Relational Knowledge Discovery. New Gener. Comput. 17(1): 3-23 (1999) - [c24]Nada Lavrac:
Machine Learning for Data Mining in Medicine. AIMDM 1999: 47-64 - [c23]Dragan Gamberger, Nada Lavrac, Ciril Groselj:
Diagnostic Rules of Increased Reliability for Critical Medical Applications. AIMDM 1999: 361-365 - [c22]Nada Lavrac:
Challenges for Inductive Logic Programming. EPIA 1999: 16-33 - [c21]Dragan Gamberger, Nada Lavrac, Ciril Groselj:
Experiments with Noise Filtering in a Medical Domain. ICML 1999: 143-151 - [c20]Nada Lavrac, Peter A. Flach, Blaz Zupan:
Rule Evaluation Measures: A Unifying View. ILP 1999: 174-185 - 1998
- [j12]Nada Lavrac, Blaz Zupan, Igor Kononenko, Matjaz Kukar, Elpida T. Keravnou:
Intelligent Data Analysis for Medical Diagnosis: Using Machine Learning and Temporal Abstraction. AI Commun. 11(3-4): 191-218 (1998) - [j11]Nada Lavrac, Dragan Gamberger, Peter D. Turney:
A Relevancy Filter for Constructive Induction. IEEE Intell. Syst. 13(2): 50-56 (1998) - [c19]Nada Lavrac:
Inductive Logic Programming for Relational Knowledge Discovery. IJCSLP 1998: 7-24 - 1997
- [j10]Darko Zupanic, Milan Hodoscek, Nada Lavrac, Igor Mozetic:
Global Energy Minimization of Small Molecules Combining Constraint Logic Programming and Molecular Mechanics. J. Chem. Inf. Comput. Sci. 37(6): 966-970 (1997) - [c18]Igor Zelic, Igor Kononenko, Nada Lavrac, Vanja Vuga:
Machine Learning Applied to Diagnosis of Sport Injuries. AIME 1997: 138-141 - [c17]Igor Zelic, Igor Kononenko, Nada Lavrac, Vanja Vuga:
Diagnosis of sport injuries with machine learning: explanation of induced decisions. CBMS 1997: 195-199 - [c16]Iztok A. Pilih, Dunja Mladenic, Nada Lavrac, Tine S. Prevec:
Using machine learning for outcome prediction of patients with severe head injury. CBMS 1997: 200-204 - [c15]Dragan Gamberger, Nada Lavrac:
Conditions for Occam's Razor Applicability and Noise Elimination. ECML 1997: 108-123 - [e3]Nada Lavrac, Saso Dzeroski:
Inductive Logic Programming, 7th International Workshop, ILP-97, Prague, Czech Republic, September 17-20, 1997, Proceedings. Lecture Notes in Computer Science 1297, Springer 1997, ISBN 3-540-63514-9 [contents] - 1996
- [j9]Nada Lavrac, Irene Weber, Darko Zupanic, Dimitar Kazakov, Olga Stepánková, Saso Dzeroski:
ILPNET Repositories on WWW: Inductive Logic Programming Systems, Datasets and Bibliography. AI Commun. 9(4): 157-206 (1996) - [j8]Luc De Raedt, Nada Lavrac:
Multiple Predicate Learning in Two Inductive Logic Programming Settings. Log. J. IGPL 4(2): 227-254 (1996) - [j7]Nada Lavrac, Stefan Wrobel:
Induktive Logikprogrammierung - Grundlagen und Techniken. Künstliche Intell. 10(3): 46-54 (1996) - [j6]Nada Lavrac, Saso Dzeroski:
A Reply to Pazzani's Book Review of "Inductive Logic Programming: Techniques and Applications". Mach. Learn. 23(1): 109-111 (1996) - [c14]Nada Lavrac, Dragan Gamberger, Peter D. Turney:
Cost-Sensitive Feature Reduction Applied to a Hybrid Genetic Algorithm. ALT 1996: 127-134 - [c13]Dragan Gamberger, Nada Lavrac, Saso Dzeroski:
Noise Elimination in Inductive Concept Learning: A Case Study in Medical Diagnosois. ALT 1996: 199-212 - [c12]Dragan Gamberger, Nada Lavrac:
Noise Detection and Elimination Applied to Noise Handling in a KRK Chess Endgame. Inductive Logic Programming Workshop 1996: 72-88 - 1995
- [j5]Nada Lavrac, Luc De Raedt:
Inductive Logic Programming: A Survey of European Research. AI Commun. 8(1): 3-19 (1995) - [e2]Nada Lavrac, Stefan Wrobel:
Machine Learning: ECML-95, 8th European Conference on Machine Learning, Heraclion, Crete, Greece, April 25-27, 1995, Proceedings. Lecture Notes in Computer Science 912, Springer 1995, ISBN 3-540-59286-5 [contents] - 1994
- [b2]Nada Lavrac, Saso Dzeroski:
Inductive logic programming - techniques and applications. Ellis Horwood series in artificial intelligence, Ellis Horwood 1994, ISBN 978-0-13-457870-5, pp. I-XIX, 1-293 - [j4]Nada Lavrac, Saso Dzeroski:
Weakening the language bias in LINUS. J. Exp. Theor. Artif. Intell. 6(1): 95-119 (1994) - [c11]Nada Lavrac:
Inductive Logic Programming. WLP 1994: 146-160 - 1993
- [j3]Nada Lavrac, Saso Dzeroski, Vladimir Pirnat, Viljem Krizman:
The utility of background knowledge in learning medical diagnostic rules. Appl. Artif. Intell. 7(3): 273-293 (1993) - [j2]Saso Dzeroski, Nada Lavrac:
Inductive Learning in Deductive Databases. IEEE Trans. Knowl. Data Eng. 5(6): 939-949 (1993) - [c10]Luc De Raedt, Nada Lavrac, Saso Dzeroski:
Multiple Predicate Learning. IJCAI 1993: 1037-1043 - [c9]Luc De Raedt, Nada Lavrac:
The Many Faces of Inductive Logic Programming. ISMIS 1993: 435-449 - 1992
- [c8]Nada Lavrac, Saso Dzeroski:
Background Knowledge and Declarative Bias in Inductive Concept Learning. AII 1992: 51-71 - [c7]Matevz Kovacic, Nada Lavrac, Marko Grobelnik, Darko Zupanic, Dunja Mladenic:
Stochastic Search in Inductive Logic Programming. ECAI 1992: 444-445 - 1991
- [c6]Nada Lavrac, Saso Dzeroski, Marko Grobelnik:
Learning Nonrecursive Definitions of Relations with LINUS. EWSL 1991: 265-281 - [c5]Saso Dzeroski, Nada Lavrac:
Learning Relations from Noisy Examples: An Empirical Comparison of LINUS and FOIL. ML 1991: 399-402 - [c4]Nada Lavrac, Saso Dzeroski, Vladimir Pirnat, Viljem Krizman:
Learning Rules for Early Diagnosis of Rheumatic Diseases. SCAI 1991: 138-149
1980 – 1989
- 1989
- [b1]Ivan Bratko, Igor Mozetic, Nada Lavrac:
KARDIO - a study in deep and qualitative knowledge for expert systems. MIT Press 1989, ISBN 978-0-262-02273-6, pp. I-XIV, 1-260 - [j1]Nada Lavrac, Igor Mozetic:
Methods for knowledge acquisition and refinement in second generation expert systems. SIGART Newsl. 108: 63-69 (1989) - 1987
- [c3]Matjaz Gams, Nada Lavrac:
Review of Five Empirical Learning Systems Within a Proposed Schemata. EWSL 1987: 46-66 - [e1]Ivan Bratko, Nada Lavrac:
Progress in Machine Learning - Proceedings of EWSL 87: 2nd European Working Session on Learning, Bled, Yugoslavia, May 1987. Sigma Press, Wilmslow 1987, ISBN 1-85058-088-X [contents] - 1986
- [c2]Ryszard S. Michalski, Igor Mozetic, Jiarong Hong, Nada Lavrac:
The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains. AAAI 1986: 1041-1047 - 1983
- [c1]Igor Mozetic, Ivan Bratko, Nada Lavrac:
An Experiment in Automatic Synthesis of Expert Knowledge Through Qualitative Modelling. Logic Programming Workshop 1983: 553-559
Coauthor Index
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