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ORCID23rd DS 2020: Thessaloniki, Greece
- Annalisa Appice
, Grigorios Tsoumakas, Yannis Manolopoulos, Stan Matwin:
Discovery Science - 23rd International Conference, DS 2020, Thessaloniki, Greece, October 19-21, 2020, Proceedings. Lecture Notes in Computer Science 12323, Springer 2020, ISBN 978-3-030-61526-0
Classification
- Riku Laine, Antti Hyttinen, Michael Mathioudakis:
Evaluating Decision Makers over Selectively Labelled Data: A Causal Modelling Approach. 3-18 - Emma Briggs, Jaakko Hollmén:
Mitigating Discrimination in Clinical Machine Learning Decision Support Using Algorithmic Processing Techniques. 19-33 - Julius Gonsior, Maik Thiele, Wolfgang Lehner:
WeakAL: Combining Active Learning and Weak Supervision. 34-49
Clustering
- Nguyen-Viet-Dung Nghiem, Christel Vrain, Thi-Bich-Hanh Dao, Ian Davidson:
Constrained Clustering via Post-processing. 53-67 - Giovanna Castellano, Gennaro Vessio:
Deep Convolutional Embedding for Painting Clustering: Case Study on Picasso's Artworks. 68-78 - Gabriella Casalino, Giovanna Castellano, Francesco Galetta, Katarzyna Kaczmarek-Majer:
Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction. 79-93 - Hadi Fanaee-T, Magne Thoresen:
Iterative Multi-mode Discretization: Applications to Co-clustering. 94-105
Data and Knowledge Representation
- Matej Martinc, Blaz Skrlj, Sergej Pirkmajer, Nada Lavrac, Bojan Cestnik, Martin Marzidovsek, Senja Pollak:
COVID-19 Therapy Target Discovery with Context-Aware Literature Mining. 109-123 - Ilin Tolovski, Saso Dzeroski, Pance Panov:
Semantic Annotation of Predictive Modelling Experiments. 124-139 - Ana Kostovska, Saso Dzeroski, Pance Panov:
Semantic Description of Data Mining Datasets: An Ontology-Based Annotation Schema. 140-155
Data Streams
- Vasileios Iosifidis, Eirini Ntoutsi:
FABBOO - Online Fairness-Aware Learning Under Class Imbalance. 159-174 - Wenbin Zhang, Albert Bifet:
FEAT: A Fairness-Enhancing and Concept-Adapting Decision Tree Classifier. 175-189 - Vítor Cerqueira, Heitor Murilo Gomes, Albert Bifet:
Unsupervised Concept Drift Detection Using a Student-Teacher Approach. 190-204
Dimensionality Reduction and Feature Selection
- Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Features. 207-216 - Jure Brence, Jovan Tanevski, Jennifer Adams, Edward Malina, Saso Dzeroski:
Learning Surrogates of a Radiative Transfer Model for the Sentinel 5P Satellite. 217-230 - Nicolas Vecoven, Jean-Michel Begon, Antonio Sutera, Pierre Geurts, Vân Anh Huynh-Thu:
Nets Versus Trees for Feature Ranking and Gene Network Inference. 231-245 - Ioulia Karagiannaki, Yannis Pantazis, Ekaterini Chatzaki, Ioannis Tsamardinos:
Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression Data. 246-261
Distributed Processing
- Apostolos Glenis, George A. Vouros:
Balancing Between Scalability and Accuracy in Time-Series Classification for Stream and Batch Settings. 265-279 - Maria Siopi, George Vlahavas, Kostas Karasavvas, Athena Vakali:
DeCStor: A Framework for Privately and Securely Sharing Files Using a Public Blockchain. 280-293 - Jan Bollenbacher, Florian Soulier, Beate Rhein, Laurenz Wiskott:
Investigating Parallelization of MAML. 294-306
Ensembles
- Alexander Tornede, Marcel Wever, Eyke Hüllermeier:
Extreme Algorithm Selection with Dyadic Feature Representation. 309-324 - Oghenejokpeme I. Orhobor, Larisa N. Soldatova, Ross D. King:
Federated Ensemble Regression Using Classification. 325-339 - Jonathan Kaufmann, Kathryn Asalone, Roberto Corizzo, Colin Saldanha, John R. Bracht, Nathalie Japkowicz:
One-Class Ensembles for Rare Genomic Sequences Identification. 340-354
Explainable and Interpretable Machine Learning
- Orestis Lampridis, Riccardo Guidotti, Salvatore Ruggieri:
Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars. 357-373 - Oghenejokpeme I. Orhobor, Joseph French, Larisa N. Soldatova, Ross D. King:
Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology. 374-385 - Noëlie Cherrier, Michael Mayo, Jean-Philippe Poli, Maxime Defurne, Franck Sabatié:
Interpretable Machine Learning with Bitonic Generalized Additive Models and Automatic Feature Construction. 386-402 - Francesca Naretto, Roberto Pellungrini, Anna Monreale, Franco Maria Nardini, Mirco Musolesi:
Predicting and Explaining Privacy Risk Exposure in Mobility Data. 403-418
Graph and Network Mining
- Kouzou Ohara, Takayasu Fushimi, Kazumi Saito, Masahiro Kimura, Hiroshi Motoda:
Maximizing Network Coverage Under the Presence of Time Constraint by Injecting Most Effective k-Links. 421-436 - Nikolaos Giarelis, Nikos Kanakaris, Nikos I. Karacapilidis:
On the Utilization of Structural and Textual Information of a Scientific Knowledge Graph to Discover Future Research Collaborations: A Link Prediction Perspective. 437-450 - Angelo Impedovo, Paolo Mignone, Corrado Loglisci, Michelangelo Ceci:
Simultaneous Process Drift Detection and Characterization with Pattern-Based Change Detectors. 451-467
Multi-target Models
- Simon Bohlender, Eneldo Loza Mencía, Moritz Kulessa:
Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains. 471-485 - Vedrana Vidulin, Saso Dzeroski:
Hierarchy Decomposition Pipeline: A Toolbox for Comparison of Model Induction Algorithms on Hierarchical Multi-label Classification Problems. 486-501 - Elia Van Wolputte, Hendrik Blockeel:
Missing Value Imputation with MERCS: A Faster Alternative to MissForest. 502-516 - Jonas Schouterden, Jesse Davis, Hendrik Blockeel:
Multi-directional Rule Set Learning. 517-532 - Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On Aggregation in Ensembles of Multilabel Classifiers. 533-547
Neural Networks and Deep Learning
- Nikolaos Virtsionis Gkalinikis, Christoforos Nalmpantis, Dimitris Vrakas:
Attention in Recurrent Neural Networks for Energy Disaggregation. 551-565 - Georgios Makridis, Philip Mavrepis, Dimosthenis Kyriazis, Ioanna Polychronou, Stathis Kaloudis:
Enhanced Food Safety Through Deep Learning for Food Recalls Prediction. 566-580 - Tongxin Hu, Vasileios Iosifidis, Wentong Liao, Hang Zhang, Michael Ying Yang, Eirini Ntoutsi, Bodo Rosenhahn:
FairNN - Conjoint Learning of Fair Representations for Fair Decisions. 581-595 - Fabrizio Angiulli, Fabio Fassetti, Luca Ferragina:
Improving Deep Unsupervised Anomaly Detection by Exploiting VAE Latent Space Distribution. 596-611
Spatial, Temporal and Spatiotemporal Data
- Ioannis Mavroudopoulos, Anastasios Gounaris:
Detecting Temporal Anomalies in Business Processes Using Distance-Based Methods. 615-629 - Alexandre Dubray, Guillaume Derval, Siegfried Nijssen, Pierre Schaus:
Mining Constrained Regions of Interest: An Optimization Approach. 630-644 - Siqi Peng, Akihiro Yamamoto:
Mining Disjoint Sequential Pattern Pairs from Tourist Trajectory Data. 645-658 - Vishnu Unnikrishnan, Yash Shah, Miro Schleicher, Mirela Strandzheva, Plamen Dimitrov, Doroteya Velikova, Rüdiger Pryss, Johannes Schobel, Winfried Schlee, Myra Spiliopoulou:
Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from? 659-673 - Leo Tisljaric, Sofia Fernandes, Tonci Caric, João Gama:
Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method. 674-688 - Arie-Willem de Leeuw, Mathieu Heijboer, Mathijs Hofmijster, Stephan van der Zwaard, Arno J. Knobbe:
Time Series Regression in Professional Road Cycling. 689-703
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