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ORCIDJohannes Fürnkranz
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- affiliation: Johannes Kepler University of Linz, Austria
- affiliation (former): TU Darmstadt, Germany
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2020 – today
- 2024
[j51]Martin Atzmueller, Johannes Fürnkranz, Tomás Kliegr, Ute Schmid:
Explainable and interpretable machine learning and data mining. Data Min. Knowl. Discov. 38(5): 2571-2595 (2024)- [j50]Stefan Heid, Jonas Hanselle, Johannes Fürnkranz, Eyke Hüllermeier
:
Learning decision catalogues for situated decision making: The case of scoring systems. Int. J. Approx. Reason. 171: 109190 (2024) - [j49]Anna-Christina Glock, Florian Sobieczky, Johannes Fürnkranz, Peter Filzmoser, Martin Jech:
Predictive change point detection for heterogeneous data. Neural Comput. Appl. 36(26): 16071-16096 (2024) - [c136]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Learning With Generalised Card Representations for "Magic: The Gathering". CoG 2024: 1-8 - [c135]Anna-Christina Glock, Johannes Fürnkranz:
Dynamic Time Warping for Phase Recognition in Tribological Sensor Data. DaWaK 2024: 245-250 - [c134]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets. KI 2024: 17-29 - [i43]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Neural Network-based Information Set Weighting for Playing Reconnaissance Blind Chess. CoRR abs/2407.05864 (2024) - [i42]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets. CoRR abs/2407.05876 (2024) - [i41]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Learning With Generalised Card Representations for "Magic: The Gathering". CoRR abs/2407.05879 (2024) - [i40]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Contrastive Learning of Preferences with a Contextual InfoNCE Loss. CoRR abs/2407.05898 (2024) - [i39]Jonas Hanselle, Stefan Heid, Johannes Fürnkranz, Eyke Hüllermeier:
Probabilistic Scoring Lists for Interpretable Machine Learning. CoRR abs/2407.21535 (2024) - 2023
- [j48]Phuong Huynh Van Quoc, Johannes Fürnkranz, Florian Beck:
Efficient learning of large sets of locally optimal classification rules. Mach. Learn. 112(2): 571-610 (2023) - [j47]Eneldo Loza Mencía, Moritz Kulessa, Simon Bohlender, Johannes Fürnkranz:
Tree-based dynamic classifier chains. Mach. Learn. 112(11): 4129-4165 (2023) - [c133]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Weighting Information Sets with Siamese Neural Networks in Reconnaissance Blind Chess. CoG 2023: 1-8 - [c132]Jonas Hanselle, Johannes Fürnkranz, Eyke Hüllermeier:
Probabilistic Scoring Lists for Interpretable Machine Learning. DS 2023: 189-203 - [c131]Florian Beck, Johannes Fürnkranz, Phuong Huynh Van Quoc:
Generalizing Conjunctive and Disjunctive Rule Learning to Learning m-of-n Concepts. ITAT 2023: 8-13 - [c130]Florian Beck, Johannes Fürnkranz, Phuong Huynh Van Quoc:
Layerwise Learning of Mixed Conjunctive and Disjunctive Rule Sets. RuleML+RR 2023: 95-109 - [i38]Phuong Huynh Van Quoc, Johannes Fürnkranz, Florian Beck:
Efficient learning of large sets of locally optimal classification rules. CoRR abs/2301.09936 (2023) - [i37]Anna-Christina Glock, Florian Sobieczky, Johannes Fürnkranz, Peter Filzmoser, Martin Jech:
Predictive change point detection for heterogeneous data. CoRR abs/2305.06630 (2023) - 2022
- [j46]Antonella Plaia, Simona Buscemi, Johannes Fürnkranz, Eneldo Loza Mencía:
Comparing Boosting and Bagging for Decision Trees of Rankings. J. Classif. 39(1): 78-99 (2022) - [j45]Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, Michael Rapp:
A flexible class of dependence-aware multi-label loss functions. Mach. Learn. 111(2): 713-737 (2022) - [c129]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess. CoG 2022: 608-611 - [c128]Phuong Huynh Van Quoc, Florian Beck, Johannes Fürnkranz:
Incremental Update of Locally Optimal Classification Rules. DS 2022: 104-113 - [c127]Johannes Fürnkranz:
Towards Deep and Interpretable Rule Learning (invited paper). ITAT 2022: 2 - [c126]Florian Beck, Johannes Fürnkranz, Phuong Huynh Van Quoc:
On the Incremental Construction of Deep Rule Theories. ITAT 2022: 21-27 - [c125]Aïssatou Diallo, Johannes Fürnkranz:
Unsupervised Alignment of Distributional Word Embeddings. KI 2022: 60-74 - [i36]Aïssatou Diallo, Johannes Fürnkranz:
GausSetExpander: A Simple Approach for Entity Set Expansion. CoRR abs/2202.13649 (2022) - [i35]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability. CoRR abs/2204.09462 (2022) - [i34]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess. CoRR abs/2208.02029 (2022) - 2021
- [j44]Tomás Kliegr, Stepán Bahník, Johannes Fürnkranz:
A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. Artif. Intell. 295: 103458 (2021) - [j43]Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
A Unifying Framework and Comparative Evaluation of Statistical and Machine Learning Approaches to Non-Specific Syndromic Surveillance. Comput. 10(3): 32 (2021) - [j42]Aïssatou Diallo, Johannes Fürnkranz:
Learning Ordinal Embedding from Sets. Entropy 23(8): 964 (2021) - [j41]Michael Rapp, Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance. Frontiers Big Data 4: 784159 (2021) - [j40]Florian Beck, Johannes Fürnkranz:
An Empirical Investigation Into Deep and Shallow Rule Learning. Frontiers Artif. Intell. 4: 689398 (2021) - [c124]Moritz Kulessa, Bennet Wittelsbach, Eneldo Loza Mencía, Johannes Fürnkranz:
Sum-Product Networks for Early Outbreak Detection of Emerging Diseases. AIME 2021: 61-71 - [c123]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking. CoG 2021: 1-8 - [c122]Jessica Fritz, Johannes Fürnkranz:
Some Chess-Specific Improvements for Perturbation-Based Saliency Maps. CoG 2021: 1-8 - [c121]Aïssatou Diallo, Johannes Fürnkranz:
Elliptical Ordinal Embedding. DS 2021: 323-333 - [c120]Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Revisiting Non-specific Syndromic Surveillance. IDA 2021: 128-140 - [c119]Florian Beck, Johannes Fürnkranz:
Beyond DNF: First Steps towards Deep Rule Learning. ITAT 2021: 61-68 - [c118]Ryan W. Gardner, Gino Perrotta, Anvay Shah, Shivaram Kalyanakrishnan, Kevin A. Wang, Gregory Clark, Timo Bertram, Johannes Fürnkranz, Martin Müller, Brady P. Garrison, Prithviraj Dasgupta, Saeid Rezaei:
The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022. NeurIPS (Competition and Demos) 2021: 119-132 - [c117]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier:
Gradient-Based Label Binning in Multi-label Classification. ECML/PKDD (3) 2021: 462-477 - [c116]Florian Beck, Johannes Fürnkranz, Phuong Huynh Van Quoc:
Structuring Rule Sets Using Binary Decision Diagrams. RuleML+RR 2021: 48-61 - [i33]Tobias Joppen, Johannes Fürnkranz:
Ordinal Monte Carlo Tree Search. CoRR abs/2101.10670 (2021) - [i32]Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Revisiting Non-Specific Syndromic Surveillance. CoRR abs/2101.12246 (2021) - [i31]Aïssatou Diallo, Johannes Fürnkranz:
Elliptical Ordinal Embedding. CoRR abs/2105.10457 (2021) - [i30]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking. CoRR abs/2105.11864 (2021) - [i29]Florian Beck, Johannes Fürnkranz:
An Investigation into Mini-Batch Rule Learning. CoRR abs/2106.10202 (2021) - [i28]Florian Beck, Johannes Fürnkranz:
An Empirical Investigation into Deep and Shallow Rule Learning. CoRR abs/2106.10254 (2021) - [i27]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier:
Gradient-based Label Binning in Multi-label Classification. CoRR abs/2106.11690 (2021) - [i26]Timo Bertram, Johannes Fürnkranz, Martin Müller:
A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems. CoRR abs/2107.04438 (2021) - [i25]Michael Rapp, Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Correlation-based Discovery of Disease Patterns for Syndromic Surveillance. CoRR abs/2110.09208 (2021) - [i24]Eneldo Loza Mencía, Moritz Kulessa, Simon Bohlender, Johannes Fürnkranz:
Tree-Based Dynamic Classifier Chains. CoRR abs/2112.06672 (2021) - 2020
- [j39]Johannes Czech, Moritz Willig, Alena Beyer, Kristian Kersting, Johannes Fürnkranz:
Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data. Frontiers Artif. Intell. 3: 24 (2020) - [j38]Johannes Fürnkranz, Tomás Kliegr, Heiko Paulheim:
On cognitive preferences and the plausibility of rule-based models. Mach. Learn. 109(4): 853-898 (2020) - [c115]Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On Aggregation in Ensembles of Multilabel Classifiers. DS 2020: 533-547 - [c114]Aïssatou Diallo, Markus Zopf, Johannes Fürnkranz:
Permutation Learning via Lehmer Codes. ECAI 2020: 1095-1102 - [c113]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía:
Conformal Rule-Based Multi-label Classification. KI 2020: 290-296 - [c112]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, Eyke Hüllermeier:
Learning Gradient Boosted Multi-label Classification Rules. ECML/PKDD (3) 2020: 124-140 - [c111]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía, Vu-Linh Nguyen, Michael Rapp:
Rule-Based Multi-label Classification: Challenges and Opportunities. RuleML+RR 2020: 3-19 - [p6]Christian Bauckhage, Johannes Fürnkranz, Gerhard Paaß:
Vertrauenswürdiges, transparentes und robustesMaschinelles Lernen. Handbuch der Künstlichen Intelligenz 2020: 571-600 - [i23]Vu-Linh Nguyen, Eyke Hüllermeier, Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On Aggregation in Ensembles of Multilabel Classifiers. CoRR abs/2006.11916 (2020) - [i22]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz, Vu-Linh Nguyen, Eyke Hüllermeier:
Learning Gradient Boosted Multi-label Classification Rules. CoRR abs/2006.13346 (2020) - [i21]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía:
Conformal Rule-Based Multi-label Classification. CoRR abs/2007.08145 (2020) - [i20]Eyke Hüllermeier, Marcel Wever, Eneldo Loza Mencía, Johannes Fürnkranz, Michael Rapp:
A Flexible Class of Dependence-aware Multi-Label Loss Functions. CoRR abs/2011.00792 (2020) - [i19]Johannes Fürnkranz, Eyke Hüllermeier, Eneldo Loza Mencía, Michael Rapp:
Learning Structured Declarative Rule Sets - A Challenge for Deep Discrete Learning. CoRR abs/2012.04377 (2020)
2010 – 2019
- 2019
- [j37]Julian Schwehr, Stefan Luthardt, Hien Q. Dang, Maren Henzel, Hermann Winner, Jürgen Adamy, Johannes Fürnkranz, Volker Willert, Benedikt Lattke, Maximilian Höpfl, Christoph Wannemacher:
The PRORETA 4 City Assistant System. Autom. 67(9): 783-798 (2019) - [c110]Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Improving the Fusion of Outbreak Detection Methods with Supervised Learning. CIBB 2019: 55-66 - [c109]Tobias Joppen, Tilman Strübig, Johannes Fürnkranz:
Ordinal Bucketing for Game Trees using Dynamic Quantile Approximation. CoG 2019: 1-8 - [c108]Aïssatou Diallo, Markus Zopf, Johannes Fürnkranz:
Learning Analogy-Preserving Sentence Embeddings for Answer Selection. CoNLL 2019: 910-919 - [c107]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On the Trade-Off Between Consistency and Coverage in Multi-label Rule Learning Heuristics. DS 2019: 96-111 - [c106]Lukas Fleckenstein, Sebastian Kauschke, Johannes Fürnkranz:
Beta Distribution Drift Detection for Adaptive Classifiers. ESANN 2019 - [c105]Jinseok Nam, Young-Bum Kim, Eneldo Loza Mencía, Sunghyun Park, Ruhi Sarikaya, Johannes Fürnkranz:
Learning Context-dependent Label Permutations for Multi-label Classification. ICML 2019: 4733-4742 - [c104]Sebastian Kauschke, Lukas Fleckenstein, Johannes Fürnkranz:
Mending is Better than Ending: Adapting Immutable Classifiers to Nonstationary Environments using Ensembles of Patches. IJCNN 2019: 1-8 - [c103]Sebastian Kauschke, David Hermann Lehmann, Johannes Fürnkranz:
Patching Deep Neural Networks for Nonstationary Environments. IJCNN 2019: 1-8 - [c102]Hien Q. Dang, Johannes Fürnkranz:
Driver Information Embedding with Siamese LSTM networks. IV 2019: 935-940 - [c101]Maryam Tavakol, Tobias Joppen, Ulf Brefeld, Johannes Fürnkranz:
Personalized Transaction Kernels for Recommendation Using MCTS. KI 2019: 338-352 - [c100]Aïssatou Diallo, Markus Zopf, Johannes Fürnkranz:
Improving Answer Selection with Analogy-Preserving Sentence Embeddings. LWDA 2019: 84-88 - [c99]Alexander Zap, Tobias Joppen, Johannes Fürnkranz:
Deep Ordinal Reinforcement Learning. ECML/PKDD (3) 2019: 3-18 - [i18]Tobias Joppen, Johannes Fürnkranz:
Ordinal Monte Carlo Tree Search. CoRR abs/1901.04274 (2019) - [i17]Alexander Zap, Tobias Joppen, Johannes Fürnkranz:
Deep Ordinal Reinforcement Learning. CoRR abs/1905.02005 (2019) - [i16]Tobias Joppen, Tilman Strübig, Johannes Fürnkranz:
Ordinal Bucketing for Game Trees using Dynamic Quantile Approximation. CoRR abs/1905.13449 (2019) - [i15]Moritz Kulessa, Eneldo Loza Mencía, Johannes Fürnkranz:
Improving Outbreak Detection with Stacking of Statistical Surveillance Methods. CoRR abs/1907.07464 (2019) - [i14]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics. CoRR abs/1908.03032 (2019) - [i13]Johannes Czech, Moritz Willig, Alena Beyer, Kristian Kersting, Johannes Fürnkranz:
Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data. CoRR abs/1908.06660 (2019) - [i12]Aïssatou Diallo, Markus Zopf, Johannes Fürnkranz:
Learning Analogy-Preserving Sentence Embeddings for Answer Selection. CoRR abs/1910.05315 (2019) - [i11]Tomás Kliegr, Stepán Bahník, Johannes Fürnkranz:
Advances in Machine Learning for the Behavioral Sciences. CoRR abs/1911.03249 (2019) - [i10]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy. CoRR abs/1911.04393 (2019) - 2018
- [j36]Tobias Joppen, Miriam Ulrike Moneke, Nils Schröder, Christian Wirth, Johannes Fürnkranz:
Informed Hybrid Game Tree Search for General Video Game Playing. IEEE Trans. Games 10(1): 78-90 (2018) - [c98]Sebastian Kauschke, Johannes Fürnkranz:
Batchwise Patching of Classifiers. AAAI 2018: 3374-3381 - [c97]Sebastian Kauschke, Max Mühlhäuser, Johannes Fürnkranz:
Leveraging Reproduction-Error Representations for Multi-Instance Classification. DS 2018: 83-95 - [c96]Sebastian Kauschke, Max Mühlhäuser, Johannes Fürnkranz:
Towards Semi-Supervised Classification of Event Streams via Denoising Autoencoders. ICMLA 2018: 131-136 - [c95]Johannes Fürnkranz, Tomás Kliegr:
The Need for Interpretability Biases. IDA 2018: 15-27 - [c94]Hien Q. Dang, Johannes Fürnkranz:
Using Past Maneuver Executions for Personalization of a Driver Model. ITSC 2018: 742-748 - [c93]Tobias Joppen, Christian Wirth, Johannes Fürnkranz:
Preference-Based Monte Carlo Tree Search. KI 2018: 327-340 - [c92]Hien Q. Dang, Johannes Fürnkranz:
Exploiting Maneuver Dependency for Personalization of a Driver Model. LWDA 2018: 93-97 - [c91]Markus Zopf, Eneldo Loza Mencía, Johannes Fürnkranz:
Which Scores to Predict in Sentence Regression for Text Summarization? NAACL-HLT 2018: 1782-1791 - [c90]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules. PAKDD (1) 2018: 29-42 - [c89]Markus Zopf, Teresa Botschen, Tobias Falke, Benjamin Heinzerling, Ana Marasovic, Todor Mihaylov, Avinesh P. V. S., Eneldo Loza Mencía, Johannes Fürnkranz, Anette Frank:
What's Important in a Text? An Extensive Evaluation of Linguistic Annotations for Summarization. SNAMS 2018: 272-277 - [i9]Johannes Fürnkranz, Tomás Kliegr, Heiko Paulheim:
On Cognitive Preferences and the Interpretability of Rule-based Models. CoRR abs/1803.01316 (2018) - [i8]Tomás Kliegr, Stepán Bahník, Johannes Fürnkranz:
A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. CoRR abs/1804.02969 (2018) - [i7]Tobias Joppen, Christian Wirth, Johannes Fürnkranz:
Preference-Based Monte Carlo Tree Search. CoRR abs/1807.06286 (2018) - [i6]Lukas Fleckenstein, Sebastian Kauschke, Johannes Fürnkranz:
Beta Distribution Drift Detection for Adaptive Classifiers. CoRR abs/1811.10900 (2018) - [i5]Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier, Michael Rapp:
Learning Interpretable Rules for Multi-label Classification. CoRR abs/1812.00050 (2018) - [i4]Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz:
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules. CoRR abs/1812.06833 (2018) - 2017
- [j35]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) - [j34]Christian Wirth, Riad Akrour, Gerhard Neumann, Johannes Fürnkranz:
A Survey of Preference-Based Reinforcement Learning Methods. J. Mach. Learn. Res. 18: 136:1-136:46 (2017) - [c88]Iryna Gurevych, Christian M. Meyer, Carsten Binnig, Johannes Fürnkranz, Kristian Kersting, Stefan Roth, Edwin Simpson:
Interactive Data Analytics for the Humanities. CICLing (1) 2017: 527-549 - [c87]Andrei Tolstikov, Frederik Janssen, Johannes Fürnkranz:
Evaluation of Different Heuristics for Accommodating Asymmetric Loss Functions in Regression. DS 2017: 67-81 - [c86]Camila González, Eneldo Loza Mencía, Johannes Fürnkranz:
Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction. DS 2017: 127-143 - [c85]Hien Q. Dang, Johannes Fürnkranz, Alexander Biedermann, Maximilian Höpfl:
Time-to-lane-change prediction with deep learning. ITSC 2017: 1-7 - [c84]Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim, Johannes Fürnkranz:
Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification. NIPS 2017: 5413-5423 - [c83]Mohammed Arif Khan, Asif Ekbal, Eneldo Loza Mencía, Johannes Fürnkranz:
Multi-objective Optimisation-Based Feature Selection for Multi-label Classification. NLDB 2017: 38-41 - [e7]Gabriele Kern-Isberner, Johannes Fürnkranz, Matthias Thimm:
KI 2017: Advances in Artificial Intelligence - 40th Annual German Conference on AI, Dortmund, Germany, September 25-29, 2017, Proceedings. Lecture Notes in Computer Science 10505, Springer 2017, ISBN 978-3-319-67189-5 [contents] - [r21]Johannes Fürnkranz:
Class Binarization. Encyclopedia of Machine Learning and Data Mining 2017: 203-204 - [r20]Johannes Fürnkranz:
Classification Rule. Encyclopedia of Machine Learning and Data Mining 2017: 209 - [r19]Johannes Fürnkranz:
Covering Algorithm. Encyclopedia of Machine Learning and Data Mining 2017: 293-294 - [r18]Johannes Fürnkranz:
Decision List. Encyclopedia of Machine Learning and Data Mining 2017: 328 - [r17]