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Johannes Fürnkranz
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- affiliation: Johannes Kepler University of Linz, Austria
- affiliation (former): TU Darmstadt, Germany
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2020 – today
- 2024
- [j52]Michael Rapp
, Johannes Fürnkranz, Eyke Hüllermeier:
On the efficient implementation of classification rule learning. Adv. Data Anal. Classif. 18(4): 851-892 (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) - [c137]Timo Bertram, Johannes Fürnkranz, Martin Müller:
Learning With Generalised Card Representations for "Magic: The Gathering". CoG 2024: 1-8 - [c136]Anna-Christina Glock, Johannes Fürnkranz:
Dynamic Time Warping for Phase Recognition in Tribological Sensor Data. DaWaK 2024: 245-250 - [c135]Florian Beck, Johannes Fürnkranz, Phuong Huynh Van Quoc:
When Characteristic Rule-based Models Should be Preferred over Discriminative Ones. ITAT 2024: 52-59 - [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
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
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
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
Comparing Boosting and Bagging for Decision Trees of Rankings. J. Classif. 39(1): 78-99 (2022) - [j45]Eyke Hüllermeier
A flexible class of dependence-aware multi-label loss functions. Mach. Learn. 111(2): 713-737 (2022) - [c129]Timo Bertram
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
Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability. CoRR abs/2204.09462 (2022) - [i34]Timo Bertram
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess. CoRR abs/2208.02029 (2022) - 2021
- [j44]Tomás Kliegr
A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. Artif. Intell. 295: 103458 (2021) - [j43]Moritz Kulessa
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
Learning Ordinal Embedding from Sets. Entropy 23(8): 964 (2021) - [j41]Michael Rapp
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
Sum-Product Networks for Early Outbreak Detection of Emerging Diseases. AIME 2021: 61-71 - [c123]Timo Bertram
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
Elliptical Ordinal Embedding. DS 2021: 323-333 - [c120]Moritz Kulessa
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
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
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
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
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
On Aggregation in Ensembles of Multilabel Classifiers. DS 2020: 533-547 - [c114]Aïssatou Diallo
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
Learning Gradient Boosted Multi-label Classification Rules. ECML/PKDD (3) 2020: 124-140 - [c111]Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía, Vu-Linh Nguyen
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
On Aggregation in Ensembles of Multilabel Classifiers. CoRR abs/2006.11916 (2020) - [i22]Michael Rapp
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
The PRORETA 4 City Assistant System. Autom. 67(9): 783-798 (2019) - [c110]Moritz Kulessa
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
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
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
Improving Outbreak Detection with Stacking of Statistical Surveillance Methods. CoRR abs/1907.07464 (2019) - [i14]Michael Rapp
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
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
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
The Need for Interpretability Biases. IDA 2018: 15-27 - [c94]Hien Q. Dang
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
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
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
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
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
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
Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction. DS 2017: 127-143 - [c85]Hien Q. Dang
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]Johannes Fürnkranz:
Decision Lists and Decision Trees. Encyclopedia of Machine Learning and Data Mining 2017: 328-329 - [r16]Johannes Fürnkranz:
Decision Stump. Encyclopedia of Machine Learning and Data Mining 2017: 330 - [r15]Johannes Fürnkranz:
Decision Tree. Encyclopedia of Machine Learning and Data Mining 2017: 330-335 - [r14]Johannes Fürnkranz:
Divide-and-Conquer Learning. Encyclopedia of Machine Learning and Data Mining 2017: 372 - [r13]Johannes Fürnkranz:
Machine Learning and Game Playing. Encyclopedia of Machine Learning and Data Mining 2017: 783-788 - [r12]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Encyclopedia of Machine Learning and Data Mining 2017: 1000-1005 - [r11]Johannes Fürnkranz:
Pruning. Encyclopedia of Machine Learning and Data Mining 2017: 1031-1032 - [r10]Johannes Fürnkranz, Eyke Hüllermeier:
Rank Correlation. Encyclopedia of Machine Learning and Data Mining 2017: 1055 - [r9]Johannes Fürnkranz:
Rule Learning. Encyclopedia of Machine Learning and Data Mining 2017: 1117-1121 - [r8]Johannes Fürnkranz:
Rule Set. Encyclopedia of Machine Learning and Data Mining 2017: 1121 - 2016
- [j33]Johannes Fürnkranz
Special Issue on Discovery Science. Inf. Sci. 329: 849-850 (2016) - [c82]Jinseok Nam, Eneldo Loza Mencía, Johannes Fürnkranz:
All-in Text: Learning Document, Label, and Word Representations Jointly. AAAI 2016: 1948-1954 - [c81]Christian Wirth, Johannes Fürnkranz, Gerhard Neumann:
Model-Free Preference-Based Reinforcement Learning. AAAI 2016: 2222-2228 - [c80]Markus Zopf, Eneldo Loza Mencía, Johannes Fürnkranz:
Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization. COLING 2016: 1071-1082 - [c79]Fabian Hirschmann, Jinseok Nam, Johannes Fürnkranz:
What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation. COLING 2016: 3199-3208 - [c78]Markus Zopf, Eneldo Loza Mencía, Johannes Fürnkranz:
Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization. CoNLL 2016: 84-94 - [c77]Sebastian Kauschke, Johannes Fürnkranz
Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototype. DS 2016: 151-166 - [c76]Julius Stecher, Frederik Janssen, Johannes Fürnkranz
Shorter Rules Are Better, Aren't They? DS 2016: 279-294 - [c75]Prateek Veeranna Sappadla, Jinseok Nam, Eneldo Loza Mencía, Johannes Fürnkranz:
Using semantic similarity for multi-label zero-shot classification of text documents. ESANN 2016 - 2015
- [j32]Johannes Fürnkranz
Editorial. Data Min. Knowl. Discov. 29(1): 1-2 (2015) - [j31]Christian Wirth, Johannes Fürnkranz
On Learning From Game Annotations. IEEE Trans. Comput. Intell. AI Games 7(3): 304-316 (2015) - [c74]Axel Schulz, Petar Ristoski, Johannes Fürnkranz, Frederik Janssen:
Event-based Clustering for Reducing Labeling Costs of Incident-Related Microposts. MUD@ICML 2015: 44-52 - [c73]Axel Schulz, Frederik Janssen, Petar Ristoski, Johannes Fürnkranz:
Event-Based Clustering for Reducing Labeling Costs of Event-related Microposts. ICWSM 2015: 686-689 - [c72]Jinseok Nam, Eneldo Loza Mencía, Hyunwoo J. Kim
Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning. ECML/PKDD (1) 2015: 102-118 - [c71]Johannes Fürnkranz
A Brief Overview of Rule Learning. RuleML 2015: 54-69 - [c70]Jinseok Nam, Johannes Fürnkranz:
On the Importance of a Hierarchy for Learning Continuous Vector Representations of a Label Space. ICLR (Workshop) 2015 - 2014
- [j30]Sang-Hyeun Park, Johannes Fürnkranz
Efficient implementation of class-based decomposition schemes for Naïve Bayes. Mach. Learn. 96(3): 295-309 (2014) - [c69]Christian Brinker, Eneldo Loza Mencía, Johannes Fürnkranz
Graded Multilabel Classification by Pairwise Comparisons. ICDM 2014: 731-736 - [c68]Jinseok Nam, Christian Kirschner, Zheng Ma, Nicolai Erbs, Susanne Neumann, Daniela Oelke, Steffen Remus, Chris Biemann, Judith Eckle-Kohler, Johannes Fürnkranz, Iryna Gurevych, Marc Rittberger, Karsten Weihe:
Knowledge Discovery in Scientific Literature. KONVENS 2014: 66-76 - [c67]Christian Wirth, Johannes Fürnkranz:
Preference Learning from Annotated Game Databases. LWA 2014: 57-68 - [c66]Julius Stecher, Frederik Janssen, Johannes Fürnkranz
Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning. ECML/PKDD (3) 2014: 114-129 - [c65]Jinseok Nam, Jungi Kim, Eneldo Loza Mencía, Iryna Gurevych, Johannes Fürnkranz
Large-Scale Multi-label Text Classification - Revisiting Neural Networks. ECML/PKDD (2) 2014: 437-452 - [i3]Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, Scott Sanner:
Preference Learning (Dagstuhl Seminar 14101). Dagstuhl Reports 4(3): 1-27 (2014) - 2013
- [j29]Eyke Hüllermeier, Johannes Fürnkranz
Editorial: Preference learning and ranking. Mach. Learn. 93(2-3): 185-189 (2013) - [c64]Christian Wirth, Johannes Fürnkranz:
EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning. ACML 2013: 483-497 - [c63]Christian Wirth, Johannes Fürnkranz
A Policy Iteration Algorithm for Learning from Preference-Based Feedback. IDA 2013: 427-437 - [e6]Johannes Fürnkranz
Discovery Science - 16th International Conference, DS 2013, Singapore, October 6-9, 2013. Proceedings. Lecture Notes in Computer Science 8140, Springer 2013, ISBN 978-3-642-40896-0 [contents] - [i2]Jinseok Nam, Jungi Kim, Iryna Gurevych, Johannes Fürnkranz:
Large-scale Multi-label Text Classification - Revisiting Neural Networks. CoRR abs/1312.5419 (2013) - 2012
- [b1]Johannes Fürnkranz
Foundations of Rule Learning. Cognitive Technologies, Springer 2012, ISBN 978-3-540-75196-0, pp. 1-298 - [j28]Sang-Hyeun Park, Johannes Fürnkranz
Efficient prediction algorithms for binary decomposition techniques. Data Min. Knowl. Discov. 24(1): 40-77 (2012) - [j27]Johannes Fürnkranz
Preference-based reinforcement learning: a formal framework and a policy iteration algorithm. Mach. Learn. 89(1-2): 123-156 (2012) - [c62]Johannes Fürnkranz
Error-Correcting Output Codes as a Transformation from Multi-Class to Multi-Label Prediction. Discovery Science 2012: 254-267 - [c61]Wouter Duivesteijn, Eneldo Loza Mencía, Johannes Fürnkranz
Multi-label LeGo - Enhancing Multi-label Classifiers with Local Patterns. IDA 2012: 114-125 - [c60]Heiko Paulheim
Unsupervised generation of data mining features from linked open data. WIMS 2012: 31:1-31:12 - 2011
- [j26]Lars Wohlrab, Johannes Fürnkranz
A review and comparison of strategies for handling missing values in separate-and-conquer rule learning. J. Intell. Inf. Syst. 36(1): 73-98 (2011) - [c59]Eyke Hüllermeier, Johannes Fürnkranz
Learning from Label Preferences. ALT 2011: 38 - [c58]Eyke Hüllermeier, Johannes Fürnkranz
Learning from Label Preferences. Discovery Science 2011: 2-17 - [c57]Jan-Nikolas Sulzmann, Johannes Fürnkranz
Rule Stacking: An Approach for Compressing an Ensemble of Rule Sets into a Single Classifier. Discovery Science 2011: 323-334 - [c56]Frederik Janssen, Johannes Fürnkranz
Heuristic Rule-Based Regression via Dynamic Reduction to Classification. IJCAI 2011: 1330-1335 - [c55]Frederik Janssen, Johannes Fürnkranz:
Heuristic Rule-Based Regression via Dynamic Reduction to Classification. LWA 2011: 48-53 - [c54]Weiwei Cheng, Johannes Fürnkranz
Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning. ECML/PKDD (1) 2011: 312-327 - 2010
- [j25]Marco Ghiglieri, Johannes Fürnkranz
Learning to Recognize Missing E-Mail Attachments. Appl. Artif. Intell. 24(5): 443-462 (2010) - [j24]Johannes Fürnkranz
Guest Editorial: Global modeling using local patterns. Data Min. Knowl. Discov. 21(1): 1-8 (2010) - [j23]Eneldo Loza Mencía, Sang-Hyeun Park, Johannes Fürnkranz
Efficient voting prediction for pairwise multilabel classification. Neurocomputing 73(7-9): 1164-1176 (2010) - [j22]Eyke Hüllermeier, Johannes Fürnkranz
On predictive accuracy and risk minimization in pairwise label ranking. J. Comput. Syst. Sci. 76(1): 49-62 (2010) - [j21]Frederik Janssen, Johannes Fürnkranz
On the quest for optimal rule learning heuristics. Mach. Learn. 78(3): 343-379 (2010) - [j20]Johannes Fürnkranz, Jan Frederik Sima:
On exploiting hierarchical label structure with pairwise classifiers. SIGKDD Explor. 12(2): 21-25 (2010) - [c53]Sang-Hyeun Park, Lorenz Weizsäcker, Johannes Fürnkranz
Exploiting Code Redundancies in ECOC. Discovery Science 2010: 266-280 - [c52]Eneldo Loza Mencía, Johannes Fürnkranz
Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain. Semantic Processing of Legal Texts 2010: 192-215 - [c51]Frederik Janssen, Johannes Fürnkranz:
Separate-and-conquer Regression. LWA 2010: 81-88 - [c50]Jan-Nikolas Sulzmann, Johannes Fürnkranz:
Probability Estimation and Aggregation for Rule Learning. LWA 2010: 143-150 - [p5]Johannes Fürnkranz
Preference Learning: An Introduction. Preference Learning 2010: 1-17 - [p4]Johannes Fürnkranz
Preference Learning and Ranking by Pairwise Comparison. Preference Learning 2010: 65-82 - [p3]Johannes Fürnkranz:
Web Mining. Data Mining and Knowledge Discovery Handbook 2010: 913-929 - [p2]Nada Lavrac, Johannes Fürnkranz
Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms. Advances in Machine Learning I 2010: 121-146 - [e5]Johannes Fürnkranz
Preference Learning. Springer 2010, ISBN 978-3-642-14124-9 [contents] - [e4]Johannes Fürnkranz, Thorsten Joachims:
Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel. Omnipress 2010 [contents] - [r7]Johannes Fürnkranz:
Decision List. Encyclopedia of Machine Learning 2010: 261 - [r6]Johannes Fürnkranz:
Decision Lists and Decision Trees. Encyclopedia of Machine Learning 2010: 261-262 - [r5]Johannes Fürnkranz:
Decision Tree. Encyclopedia of Machine Learning 2010: 263-267 - [r4]Johannes Fürnkranz:
Machine Learning and Game Playing. Encyclopedia of Machine Learning 2010: 633-637 - [r3]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Encyclopedia of Machine Learning 2010: 789-795 - [r2]Johannes Fürnkranz:
Pruning. Encyclopedia of Machine Learning 2010: 817 - [r1]Johannes Fürnkranz:
Rule Learning. Encyclopedia of Machine Learning 2010: 875-879
2000 – 2009
- 2009
- [c49]Jan-Nikolas Sulzmann, Johannes Fürnkranz
An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules. Discovery Science 2009: 317-331 - [c48]Eneldo Loza Mencía, Sang-Hyeun Park, Johannes Fürnkranz:
Efficient voting prediction for pairwise multilabel classification. ESANN 2009 - [c47]Immanuel Schweizer, Kamill Panitzek, Sang-Hyeun Park, Johannes Fürnkranz
An Exploitative Monte-Carlo Poker Agent. KI 2009: 65-72 - [c46]Immanuel Schweizer, Kamill Panitzek, Sang-Hyeun Park, Johannes Fürnkranz:
An Exploitative Monte-Carlo Poker Agent. LWA 2009: KDML:100-104 - [c45]Eneldo Loza Mencía, Sang-Hyeun Park, Johannes Fürnkranz:
Efficient Voting Prediction for Pairwise Multilabel Classification. LWA 2009: KDML:72-75 - [c44]Lorenz Weizsäcker, Johannes Fürnkranz:
On Table Extraction from Text Sources with Markups. LWA 2009: WIR:1-8 - [c43]Sang-Hyeun Park, Johannes Fürnkranz
Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification. ECML/PKDD (2) 2009: 189-204 - [c42]Johannes Fürnkranz
Binary Decomposition Methods for Multipartite Ranking. ECML/PKDD (1) 2009: 359-374 - [c41]Frederik Janssen, Johannes Fürnkranz:
A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning. SDM 2009: 329-340 - 2008
- [j19]Eyke Hüllermeier, Johannes Fürnkranz
Label ranking by learning pairwise preferences. Artif. Intell. 172(16-17): 1897-1916 (2008) - [j18]Sacha Droste, Johannes Fürnkranz:
Learning the Piece Values for Three Chess Variants. J. Int. Comput. Games Assoc. 31(4): 209-233 (2008) - [j17]Johannes Fürnkranz
Multilabel classification via calibrated label ranking. Mach. Learn. 73(2): 133-153 (2008) - [c40]Frederik Janssen, Johannes Fürnkranz:
An Empirical Investigation of the Trade-Off between Consistency and Coverage in Rule Learning Heuristics. Discovery Science 2008: 40-51 - [c39]Eneldo Loza Mencía, Johannes Fürnkranz
Pairwise learning of multilabel classifications with perceptrons. IJCNN 2008: 2899-2906 - [c38]Frederik Janssen, Johannes Fürnkranz:
A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning. LWA 2008: 42-49 - [c37]Jan-Nikolas Sulzmann, Johannes Fürnkranz:
A Comparison of Techniques for Selecting and Combining Class Association Rules. LWA 2008: 87-93 - [c36]Eneldo Loza Mencía, Johannes Fürnkranz
Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain. ECML/PKDD (2) 2008: 50-65 - [c35]Dragan Gamberger, Nada Lavrac, Johannes Fürnkranz
Handling Unknown and Imprecise Attribute Values in Propositional Rule Learning: A Feature-Based Approach. PRICAI 2008: 636-645 - 2007
- [c34]Jan-Nikolas Sulzmann, Johannes Fürnkranz, Eyke Hüllermeier:
On Pairwise Naive Bayes Classifiers. ECML 2007: 371-381 - [c33]Eyke Hüllermeier, Johannes Fürnkranz:
On Minimizing the Position Error in Label Ranking. ECML 2007: 583-590 - [c32]Sang-Hyeun Park, Johannes Fürnkranz:
Efficient Pairwise Classification. ECML 2007: 658-665 - [c31]Frederik Janssen, Johannes Fürnkranz
On Meta-Learning Rule Learning Heuristics. ICDM 2007: 529-534 - [c30]Eneldo Loza Mencía, Johannes Fürnkranz:
An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain. LWA 2007: 126-132 - [c29]Frederik Janssen, Johannes Fürnkranz:
Meta-Learning Rule Learning Heuristics. LWA 2007: 167-174 - 2006
- [j16]Michael H. Bowling, Johannes Fürnkranz
Machine learning and games. Mach. Learn. 63(3): 211-215 (2006) - [c28]Klaus Brinker, Johannes Fürnkranz, Eyke Hüllermeier:
A Unified Model for Multilabel Classification and Ranking. ECAI 2006: 489-493 - [c27]Frederik Janssen, Johannes Fürnkranz:
On Trading Off Consistency and Coverage in Inductive Rule Learning. LWA 2006: 306-313 - [e3]Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou:
Machine Learning: ECML 2006, 17th European Conference on Machine Learning, Berlin, Germany, September 18-22, 2006, Proceedings. Lecture Notes in Computer Science 4212, Springer 2006, ISBN 3-540-45375-X [contents] - [e2]Johannes Fürnkranz, Tobias Scheffer, Myra Spiliopoulou:
Knowledge Discovery in Databases: PKDD 2006, 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Berlin, Germany, September 18-22, 2006, Proceedings. Lecture Notes in Computer Science 4213, Springer 2006, ISBN 3-540-45374-1 [contents] - 2005
- [j15]Johannes Fürnkranz, Eyke Hüllermeier:
Preference Learning. Künstliche Intell. 19(1): 60- (2005) - [j14]Johannes Fürnkranz
ROC 'n' Rule Learning - Towards a Better Understanding of Covering Algorithms. Mach. Learn. 58(1): 39-77 (2005) - [c26]Hervé Utard, Johannes Fürnkranz
Link-Local Features for Hypertext Classification. EWMF/KDO 2005: 51-64 - [c25]Eyke Hüllermeier, Johannes Fürnkranz:
Learning Label Preferences: Ranking Error Versus Position Error. IDA 2005: 180-191 - [c24]Eyke Hüllermeier, Johannes Fürnkranz, Jürgen Beringer:
On Position Error and Label Ranking through Iterated Choice. LWA 2005: 158-163 - [p1]Johannes Fürnkranz:
Web Mining. The Data Mining and Knowledge Discovery Handbook 2005: 899-920 - [e1]Mathias Bauer, Boris Brandherm, Johannes Fürnkranz, Gunter Grieser, Andreas Hotho, Andreas Jedlitschka, Alexander Kröner:
Lernen, Wissensentdeckung und Adaptivität (LWA) 2005, GI Workshops, Saarbrücken, October 10th-12th, 2005. DFKI 2005 [contents] - 2004
- [c23]Johannes Fürnkranz:
From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms. Local Pattern Detection 2004: 20-38 - [c22]Johannes Fürnkranz, Peter A. Flach
An Analysis of Stopping and Filtering Criteria for Rule Learning. ECML 2004: 123-133 - [c21]Eyke Hüllermeier, Johannes Fürnkranz
Ranking by pairwise comparison a note on risk minimization. FUZZ-IEEE 2004: 97-102 - [c20]Johannes Fürnkranz:
Modeling Rule Precision. LWA 2004: 147-154 - 2003
- [j13]Johannes Fürnkranz:
Round robin ensembles. Intell. Data Anal. 7(5): 385-403 (2003) - [c19]Johannes Fürnkranz, Eyke Hüllermeier:
Pairwise Preference Learning and Ranking. ECML 2003: 145-156 - [c18]Johannes Fürnkranz, Peter A. Flach:
An Analysis of Rule Evaluation Metrics. ICML 2003: 202-209 - [c17]Petr Savický, Johannes Fürnkranz:
Combining Pairwise Classifiers with Stacking. IDA 2003: 219-229 - 2002
- [j12]Johannes Fürnkranz
User Profiling for the MELVIL Knowledge Retrieval System. Appl. Artif. Intell. 16(4): 243-281 (2002) - [j11]Johannes Fürnkranz
Hyperlink ensembles: a case study in hypertext classification. Inf. Fusion 3(4): 299-312 (2002) - [j10]Johannes Fürnkranz:
Round Robin Classification. J. Mach. Learn. Res. 2: 721-747 (2002) - [c16]Johannes Fürnkranz:
A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning. ALT 2002: 263-277 - [c15]Johannes Fürnkranz:
Pairwise Classification as an Ensemble Technique. ECML 2002: 97-110 - 2001
- [c14]Johannes Fürnkranz:
Round Robin Rule Learning. ICML 2001: 146-153 - [c13]Alexander K. Seewald, Johannes Fürnkranz
An Evaluation of Grading Classifiers. IDA 2001: 115-124 - [c12]Hendrik Blockeel
Detecting Temporal Change in Event Sequences: An Application to Demographic Data. PKDD 2001: 29-41 - 2000
- [j9]Klaus Kovar, Johannes Fürnkranz
Searching for Patterns in Political Event Sequences: Experiments with the Keds Database. Cybern. Syst. 31(6): 649-668 (2000) - [c11]Johannes Fürnkranz, Bernhard Pfahringer, Hermann Kaindl, Stefan Kramer:
Learning to Use Operational Advice. ECAI 2000: 291-295
1990 – 1999
- 1999
- [j8]Johannes Fürnkranz
Separate-and-Conquer Rule Learning. Artif. Intell. Rev. 13(1): 3-54 (1999) - [j7]Johannes Fürnkranz, Moroslaw Kubat:
Report on the Machine-Learning in Game-Playing Workshop. J. Int. Comput. Games Assoc. 22(3): 178-179 (1999) - [c10]Johannes Fürnkranz
Exploiting Structural Information for Text Classification on the WWW. IDA 1999: 487-498 - 1998
- [j6]Johannes Fürnkranz
Guest Editorial: First-Order Knowledge Discovery in Databases. Appl. Artif. Intell. 12(5): 345-361 (1998) - [j5]Franz-Günter Winkler, Johannes Fürnkranz:
A Hypothesis on the Divergence of AI Research. J. Int. Comput. Games Assoc. 21(1): 3-13 (1998) - [j4]Johannes Fürnkranz:
Integrative Windowing. J. Artif. Intell. Res. 8: 129-164 (1998) - [i1]Johannes Fürnkranz:
Integrative Windowing. CoRR cs.AI/9805101 (1998) - 1997
- [j3]Johannes Fürnkranz
Knowledge Discovery in International Conflict Databases. Appl. Artif. Intell. 11(2): 91-118 (1997) - [j2]Johannes Fürnkranz
Pruning Algorithms for Rule Learning. Mach. Learn. 27(2): 139-172 (1997) - [c9]Franz-Günter Winkler, Johannes Fürnkranz:
On Effort in AI Research: A Description Along Two Dimensions. Deep Blue Versus Kasparov: The Significance for Artificial Intelligence 1997: 56-62 - [c8]Johannes Fürnkranz:
More Efficient Windowing. AAAI/IAAI 1997: 509-514 - [c7]Johannes Fürnkranz:
Noise-Tolerant Windowing. IJCAI (2) 1997: 852-859 - 1996
- [j1]Johannes Fürnkranz:
Machine Learning in Computer Chess: The Next Generation. J. Int. Comput. Games Assoc. 19(3): 147-161 (1996) - [c6]Robert Trappl, Johannes Fürnkranz, Johann Petrak:
Digging for Peace: Using Machine Learning Methods for Assessing International Conflict Databases. ECAI 1996: 453-457 - 1995
- [c5]Johannes Fürnkranz:
A Tight Integration of Pruning and Learning (Extended Abstract). ECML 1995: 291-294 - 1994
- [c4]Johannes Fürnkranz:
Top-Down Pruning in Relational Learning. ECAI 1994: 453-457 - [c3]Johannes Fürnkranz:
FOSSIL: A Robust Relational Learner. ECML 1994: 122-137 - [c2]Johannes Fürnkranz, Gerhard Widmer:
Incremental Reduced Error Pruning. ICML 1994: 70-77 - [c1]Johannes Fürnkranz:
A Comparison of Pruning Methods for Relational Concept Learning. KDD Workshop 1994: 371-382
Coauthor Index
[j47] [j46] [j45] [j43] [j41] [c124] [c120] [c117] [i32] [i27] [i25] [i24] [c115] [c113] [c112] [c111] [i23] [i22] [i21] [i20] [i19] [c110] [c107] [c105] [i15] [i14] [i10] [c91] [c90] [c89] [i5] [i4] [c86] [c84] [c83] [c82] [c80] [c78] [c75] [c72] [c69] [c65] [c61] [j23] [c52] [c48] [c45] [j17] [c39] [c36] [c30]
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