Top-Down Pruning in Relational Learning. - Johannes Fürnkranz (ECAI, 1994)

FOSSIL: A Robust Relational Learner. - Johannes Fürnkranz (ECML, 1994)

Incremental Reduced Error Pruning. - Johannes Fürnkranz; Gerhard Widmer (ICML, 1994)

A Comparison of Pruning Methods for Relational Concept Learning. - Johannes Fürnkranz (KDD Workshop, 1994)

A Tight Integration of Pruning and Learning (Extended Abstract). - Johannes Fürnkranz (ECML, 1995)

Digging for Peace: Using Machine Learning Methods for Assessing International Conflict Databases. - Robert Trappl; Johannes Fürnkranz; Johann Petrak (ECAI, 1996)

Machine Learning in Computer Chess: The Next Generation. - Johannes Fürnkranz (J. Int. Comput. Games Assoc., 1996)

More Efficient Windowing. - Johannes Fürnkranz (AAAI/IAAI, 1997)

On Effort in AI Research: A Description Along Two Dimensions. - Franz-Günter Winkler; Johannes Fürnkranz (Deep Blue Versus Kasparov: The Significance for Artificial Intelligence, 1997)

Noise-Tolerant Windowing. - Johannes Fürnkranz (IJCAI (2), 1997)

Knowledge Discovery in International Conflict Databases. - Johannes Fürnkranz; Johann Petrak; Robert Trappl (Appl. Artif. Intell., 1997)

Pruning Algorithms for Rule Learning. - Johannes Fürnkranz (Mach. Learn., 1997)

Guest Editorial: First-Order Knowledge Discovery in Databases. - Johannes Fürnkranz; Bernhard Pfahringer (Appl. Artif. Intell., 1998)

A Hypothesis on the Divergence of AI Research. - Franz-Günter Winkler; Johannes Fürnkranz (J. Int. Comput. Games Assoc., 1998)

Integrative Windowing. - Johannes Fürnkranz (J. Artif. Intell. Res., 1998)

Exploiting Structural Information for Text Classification on the WWW. - Johannes Fürnkranz (IDA, 1999)

Separate-and-Conquer Rule Learning. - Johannes Fürnkranz (Artif. Intell. Rev., 1999)

Report on the Machine-Learning in Game-Playing Workshop. - Johannes Fürnkranz; Moroslaw Kubat (J. Int. Comput. Games Assoc., 1999)

Learning to Use Operational Advice. - Johannes Fürnkranz; Bernhard Pfahringer; Hermann Kaindl; Stefan Kramer 001 (ECAI, 2000)

Searching for Patterns in Political Event Sequences: Experiments with the Keds Database. - Klaus Kovar; Johannes Fürnkranz; Johann Petrak; Bernhard Pfahringer; Robert Trappl; Gerhard Widmer (Cybern. Syst., 2000)

Round Robin Rule Learning. - Johannes Fürnkranz (ICML, 2001)

An Evaluation of Grading Classifiers. - Alexander K. Seewald; Johannes Fürnkranz (IDA, 2001)

Detecting Temporal Change in Event Sequences: An Application to Demographic Data. - Hendrik Blockeel; Johannes Fürnkranz; Alexia Prskawetz; Francesco C. Billari (PKDD, 2001)

A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning. - Johannes Fürnkranz (ALT, 2002)

Pairwise Classification as an Ensemble Technique. - Johannes Fürnkranz (ECML, 2002)

User Profiling for the MELVIL Knowledge Retrieval System. - Johannes Fürnkranz; Christian Holzbaur; Robert Temel (Appl. Artif. Intell., 2002)

Hyperlink ensembles: a case study in hypertext classification. - Johannes Fürnkranz (Inf. Fusion, 2002)

Round Robin Classification. - Johannes Fürnkranz (J. Mach. Learn. Res., 2002)

Pairwise Preference Learning and Ranking. - Johannes Fürnkranz; Eyke Hüllermeier (ECML, 2003)

An Analysis of Rule Evaluation Metrics. - Johannes Fürnkranz; Peter A. Flach (ICML, 2003)

Combining Pairwise Classifiers with Stacking. - Petr Savický; Johannes Fürnkranz (IDA, 2003)

Round robin ensembles. - Johannes Fürnkranz (Intell. Data Anal., 2003)

From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms. - Johannes Fürnkranz (Local Pattern Detection, 2004)

An Analysis of Stopping and Filtering Criteria for Rule Learning. - Johannes Fürnkranz; Peter A. Flach (ECML, 2004)

Ranking by pairwise comparison a note on risk minimization. - Eyke Hüllermeier; Johannes Fürnkranz (FUZZ-IEEE, 2004)

Modeling Rule Precision. - Johannes Fürnkranz (LWA, 2004)

Web Mining. - Johannes Fürnkranz (The Data Mining and Knowledge Discovery Handbook, 2005)

Link-Local Features for Hypertext Classification. - Hervé Utard; Johannes Fürnkranz (EWMF/KDO, 2005)

Learning Label Preferences: Ranking Error Versus Position Error. - Eyke Hüllermeier; Johannes Fürnkranz (IDA, 2005)

On Position Error and Label Ranking through Iterated Choice. - Eyke Hüllermeier; Johannes Fürnkranz; Jürgen Beringer (LWA, 2005)

Preference Learning. - Johannes Fürnkranz; Eyke Hüllermeier (Künstliche Intell., 2005)

ROC 'n' Rule Learning - Towards a Better Understanding of Covering Algorithms. - Johannes Fürnkranz; Peter A. Flach (Mach. Learn., 2005)

A Unified Model for Multilabel Classification and Ranking. - Klaus Brinker; Johannes Fürnkranz; Eyke Hüllermeier (ECAI, 2006)

On Trading Off Consistency and Coverage in Inductive Rule Learning. - Frederik Janssen; Johannes Fürnkranz (LWA, 2006)

Machine learning and games. - Michael H. Bowling; Johannes Fürnkranz; Thore Graepel; Ron Musick (Mach. Learn., 2006)

On Minimizing the Position Error in Label Ranking. - Eyke Hüllermeier; Johannes Fürnkranz (ECML, 2007)

Efficient Pairwise Classification. - Sang-Hyeun Park; Johannes Fürnkranz (ECML, 2007)

On Pairwise Naive Bayes Classifiers. - Jan-Nikolas Sulzmann; Johannes Fürnkranz; Eyke Hüllermeier (ECML, 2007)

On Meta-Learning Rule Learning Heuristics. - Frederik Janssen; Johannes Fürnkranz (ICDM, 2007)

An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain. - Eneldo Loza Mencía; Johannes Fürnkranz (LWA, 2007)

An Empirical Investigation of the Trade-Off between Consistency and Coverage in Rule Learning Heuristics. - Frederik Janssen; Johannes Fürnkranz (Discovery Science, 2008)

Pairwise learning of multilabel classifications with perceptrons. - Eneldo Loza Mencía; Johannes Fürnkranz (IJCNN, 2008)

A Comparison of Techniques for Selecting and Combining Class Association Rules. - Jan-Nikolas Sulzmann; Johannes Fürnkranz (LWA, 2008)

Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain. - Eneldo Loza Mencía; Johannes Fürnkranz (ECML/PKDD (2), 2008)

Handling Unknown and Imprecise Attribute Values in Propositional Rule Learning: A Feature-Based Approach. - Dragan Gamberger; Nada Lavrac; Johannes Fürnkranz (PRICAI, 2008)

Label ranking by learning pairwise preferences. - Eyke Hüllermeier; Johannes Fürnkranz; Weiwei Cheng; Klaus Brinker (Artif. Intell., 2008)

Learning the Piece Values for Three Chess Variants. - Sacha Droste; Johannes Fürnkranz (J. Int. Comput. Games Assoc., 2008)

Multilabel classification via calibrated label ranking. - Johannes Fürnkranz; Eyke Hüllermeier; Eneldo Loza Mencía; Klaus Brinker (Mach. Learn., 2008)

An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules. - Jan-Nikolas Sulzmann; Johannes Fürnkranz (Discovery Science, 2009)

An Exploitative Monte-Carlo Poker Agent. - Immanuel Schweizer; Kamill Panitzek; Sang-Hyeun Park; Johannes Fürnkranz (KI, 2009)

On Table Extraction from Text Sources with Markups. - Lorenz Weizsäcker; Johannes Fürnkranz (LWA, 2009)

Binary Decomposition Methods for Multipartite Ranking. - Johannes Fürnkranz; Eyke Hüllermeier; Stijn Vanderlooy (ECML/PKDD (1), 2009)

Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification. - Sang-Hyeun Park; Johannes Fürnkranz (ECML/PKDD (2), 2009)

A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning. - Frederik Janssen; Johannes Fürnkranz (SDM, 2009)

Preference Learning: An Introduction. - Johannes Fürnkranz; Eyke Hüllermeier (Preference Learning, 2010)

Preference Learning and Ranking by Pairwise Comparison. - Johannes Fürnkranz; Eyke Hüllermeier (Preference Learning, 2010)

Exploiting Code Redundancies in ECOC. - Sang-Hyeun Park; Lorenz Weizsäcker; Johannes Fürnkranz (Discovery Science, 2010)

Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain. - Eneldo Loza Mencía; Johannes Fürnkranz (Semantic Processing of Legal Texts, 2010)

Separate-and-conquer Regression. - Frederik Janssen; Johannes Fürnkranz (LWA, 2010)

Probability Estimation and Aggregation for Rule Learning. - Jan-Nikolas Sulzmann; Johannes Fürnkranz (LWA, 2010)

Learning to Recognize Missing E-Mail Attachments. - Marco Ghiglieri; Johannes Fürnkranz (Appl. Artif. Intell., 2010)

Guest Editorial: Global modeling using local patterns. - Johannes Fürnkranz; Arno J. Knobbe (Data Min. Knowl. Discov., 2010)

Efficient voting prediction for pairwise multilabel classification. - Eneldo Loza Mencía; Sang-Hyeun Park; Johannes Fürnkranz (Neurocomputing, 2010)

On predictive accuracy and risk minimization in pairwise label ranking. - Eyke Hüllermeier; Johannes Fürnkranz (J. Comput. Syst. Sci., 2010)

On the quest for optimal rule learning heuristics. - Frederik Janssen; Johannes Fürnkranz (Mach. Learn., 2010)

On exploiting hierarchical label structure with pairwise classifiers. - Johannes Fürnkranz; Jan Frederik Sima (SIGKDD Explor., 2010)

Web Mining. - Johannes Fürnkranz (Data Mining and Knowledge Discovery Handbook, 2010)

Decision List. - Johannes Fürnkranz (Encyclopedia of Machine Learning, 2010)

Decision Lists and Decision Trees. - Johannes Fürnkranz (Encyclopedia of Machine Learning, 2010)

Decision Tree. - Johannes Fürnkranz (Encyclopedia of Machine Learning, 2010)

Machine Learning and Game Playing. - Johannes Fürnkranz (Encyclopedia of Machine Learning, 2010)

Pruning. - Johannes Fürnkranz (Encyclopedia of Machine Learning, 2010)

Rule Learning. - Johannes Fürnkranz (Encyclopedia of Machine Learning, 2010)

Preference Learning. - Johannes Fürnkranz; Eyke Hüllermeier (Encyclopedia of Machine Learning, 2010)

Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms. - Nada Lavrac; Johannes Fürnkranz; Dragan Gamberger (Advances in Machine Learning I, 2010)

Learning from Label Preferences. - Eyke Hüllermeier; Johannes Fürnkranz (ALT, 2011)

Rule Stacking: An Approach for Compressing an Ensemble of Rule Sets into a Single Classifier. - Jan-Nikolas Sulzmann; Johannes Fürnkranz (Discovery Science, 2011)

Heuristic Rule-Based Regression via Dynamic Reduction to Classification. - Frederik Janssen; Johannes Fürnkranz (IJCAI, 2011)

Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning. - Weiwei Cheng; Johannes Fürnkranz; Eyke Hüllermeier; Sang-Hyeun Park (ECML/PKDD (1), 2011)

A review and comparison of strategies for handling missing values in separate-and-conquer rule learning. - Lars Wohlrab; Johannes Fürnkranz (J. Intell. Inf. Syst., 2011)

Error-Correcting Output Codes as a Transformation from Multi-Class to Multi-Label Prediction. - Johannes Fürnkranz; Sang-Hyeun Park (Discovery Science, 2012)

Multi-label LeGo - Enhancing Multi-label Classifiers with Local Patterns. - Wouter Duivesteijn; Eneldo Loza Mencía; Johannes Fürnkranz; Arno J. Knobbe (IDA, 2012)

Unsupervised generation of data mining features from linked open data. - Heiko Paulheim; Johannes Fürnkranz (WIMS, 2012)

Efficient prediction algorithms for binary decomposition techniques. - Sang-Hyeun Park; Johannes Fürnkranz (Data Min. Knowl. Discov., 2012)

Preference-based reinforcement learning: a formal framework and a policy iteration algorithm. - Johannes Fürnkranz; Eyke Hüllermeier; Weiwei Cheng; Sang-Hyeun Park (Mach. Learn., 2012)

Foundations of Rule Learning - Johannes Fürnkranz; Dragan Gamberger; Nada Lavrac (Springer, 2012)

EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning. - Christian Wirth 001; Johannes Fürnkranz (ACML, 2013)

A Policy Iteration Algorithm for Learning from Preference-Based Feedback. - Christian Wirth 001; Johannes Fürnkranz (IDA, 2013)

Large-scale Multi-label Text Classification - Revisiting Neural Networks. - Jinseok Nam; Jungi Kim; Iryna Gurevych; Johannes Fürnkranz (CoRR, 2013)

Editorial: Preference learning and ranking. - Eyke Hüllermeier; Johannes Fürnkranz (Mach. Learn., 2013)

Graded Multilabel Classification by Pairwise Comparisons. - Christian Brinker; Eneldo Loza Mencía; Johannes Fürnkranz (ICDM, 2014)

Knowledge Discovery in Scientific Literature. - 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; et al. (KONVENS, 2014)

Preference Learning from Annotated Game Databases. - Christian Wirth 001; Johannes Fürnkranz (LWA, 2014)

Large-Scale Multi-label Text Classification - Revisiting Neural Networks. - Jinseok Nam; Jungi Kim; Eneldo Loza Mencía; Iryna Gurevych; Johannes Fürnkranz (ECML/PKDD (2), 2014)

Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning. - Julius Stecher; Frederik Janssen; Johannes Fürnkranz (ECML/PKDD (3), 2014)

Preference Learning (Dagstuhl Seminar 14101). - Johannes Fürnkranz; Eyke Hüllermeier; Cynthia Rudin; Roman Slowinski; Scott Sanner (Dagstuhl Reports, 2014)

Efficient implementation of class-based decomposition schemes for Naïve Bayes. - Sang-Hyeun Park; Johannes Fürnkranz (Mach. Learn., 2014)

Event-based Clustering for Reducing Labeling Costs of Incident-Related Microposts. - Axel Schulz 001; Petar Ristoski; Johannes Fürnkranz; Frederik Janssen (MUD@ICML, 2015)

Event-Based Clustering for Reducing Labeling Costs of Event-related Microposts. - Axel Schulz 001; Frederik Janssen; Petar Ristoski; Johannes Fürnkranz (ICWSM, 2015)

Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning. - Jinseok Nam; Eneldo Loza Mencía; Hyunwoo J. Kim; Johannes Fürnkranz (ECML/PKDD (1), 2015)

A Brief Overview of Rule Learning. - Johannes Fürnkranz; Tomás Kliegr (RuleML, 2015)

On the Importance of a Hierarchy for Learning Continuous Vector Representations of a Label Space. - Jinseok Nam; Johannes Fürnkranz (ICLR (Workshop), 2015)

Editorial. - Johannes Fürnkranz (Data Min. Knowl. Discov., 2015)

On Learning From Game Annotations. - Christian Wirth 001; Johannes Fürnkranz (IEEE Trans. Comput. Intell. AI Games, 2015)

All-in Text: Learning Document, Label, and Word Representations Jointly. - Jinseok Nam; Eneldo Loza Mencía; Johannes Fürnkranz (AAAI, 2016)

Model-Free Preference-Based Reinforcement Learning. - Christian Wirth 001; Johannes Fürnkranz; Gerhard Neumann (AAAI, 2016)

What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation. - Fabian Hirschmann; Jinseok Nam; Johannes Fürnkranz (COLING, 2016)

Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization. - Markus Zopf; Eneldo Loza Mencía; Johannes Fürnkranz (COLING, 2016)

Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization. - Markus Zopf; Eneldo Loza Mencía; Johannes Fürnkranz (CoNLL, 2016)

Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototype. - Sebastian Kauschke; Johannes Fürnkranz; Frederik Janssen (DS, 2016)

Shorter Rules Are Better, Aren't They? - Julius Stecher; Frederik Janssen; Johannes Fürnkranz (DS, 2016)

Using semantic similarity for multi-label zero-shot classification of text documents. - Prateek Veeranna Sappadla; Jinseok Nam; Eneldo Loza Mencía; Johannes Fürnkranz (ESANN, 2016)

Special Issue on Discovery Science. - Johannes Fürnkranz; Eyke Hüllermeier (Inf. Sci., 2016)

Interactive Data Analytics for the Humanities. - Iryna Gurevych; Christian M. Meyer; Carsten Binnig; Johannes Fürnkranz; Kristian Kersting; Stefan Roth 001; Edwin Simpson (CICLing (1), 2017)

Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction. - Camila González; Eneldo Loza Mencía; Johannes Fürnkranz (DS, 2017)

Evaluation of Different Heuristics for Accommodating Asymmetric Loss Functions in Regression. - Andrei Tolstikov; Frederik Janssen; Johannes Fürnkranz (DS, 2017)

Time-to-lane-change prediction with deep learning. - Hien Q. Dang; Johannes Fürnkranz; Alexander Biedermann; Maximilian Höpfl (ITSC, 2017)

Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification. - Jinseok Nam; Eneldo Loza Mencía; Hyunwoo J. Kim; Johannes Fürnkranz (NIPS, 2017)

Multi-objective Optimisation-Based Feature Selection for Multi-label Classification. - Mohammed Arif Khan; Asif Ekbal; Eneldo Loza Mencía; Johannes Fürnkranz (NLDB, 2017)

Refinement and selection heuristics in subgroup discovery and classification rule learning. - Anita Valmarska; Nada Lavrac; Johannes Fürnkranz; Marko Robnik-Sikonja (Expert Syst. Appl., 2017)

A Survey of Preference-Based Reinforcement Learning Methods. - Christian Wirth 001; Riad Akrour; Gerhard Neumann; Johannes Fürnkranz (J. Mach. Learn. Res., 2017)

Class Binarization. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Classification Rule. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Covering Algorithm. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Decision List. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Decision Lists and Decision Trees. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Decision Stump. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Decision Tree. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Divide-and-Conquer Learning. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Machine Learning and Game Playing. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Pruning. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Rule Learning. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Rule Set. - Johannes Fürnkranz (Encyclopedia of Machine Learning and Data Mining, 2017)

Preference Learning. - Johannes Fürnkranz; Eyke Hüllermeier (Encyclopedia of Machine Learning and Data Mining, 2017)

Rank Correlation. - Johannes Fürnkranz; Eyke Hüllermeier (Encyclopedia of Machine Learning and Data Mining, 2017)

Batchwise Patching of Classifiers. - Sebastian Kauschke; Johannes Fürnkranz (AAAI, 2018)

Leveraging Reproduction-Error Representations for Multi-Instance Classification. - Sebastian Kauschke; Max Mühlhäuser; Johannes Fürnkranz (DS, 2018)

Towards Semi-Supervised Classification of Event Streams via Denoising Autoencoders. - Sebastian Kauschke; Max Mühlhäuser; Johannes Fürnkranz (ICMLA, 2018)

The Need for Interpretability Biases. - Johannes Fürnkranz; Tomás Kliegr (IDA, 2018)

Using Past Maneuver Executions for Personalization of a Driver Model. - Hien Q. Dang; Johannes Fürnkranz (ITSC, 2018)

Preference-Based Monte Carlo Tree Search. - Tobias Joppen; Christian Wirth 001; Johannes Fürnkranz (KI, 2018)

Exploiting Maneuver Dependency for Personalization of a Driver Model. - Hien Q. Dang; Johannes Fürnkranz (LWDA, 2018)

Which Scores to Predict in Sentence Regression for Text Summarization? - Markus Zopf; Eneldo Loza Mencía; Johannes Fürnkranz (NAACL-HLT, 2018)

Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules. - Michael Rapp; Eneldo Loza Mencía; Johannes Fürnkranz (PAKDD (1), 2018)

What's Important in a Text? An Extensive Evaluation of Linguistic Annotations for Summarization. - Markus Zopf; Teresa Botschen; Tobias Falke; Benjamin Heinzerling; Ana Marasovic; Todor Mihaylov; Avinesh P. V. S.; Eneldo Loza Mencía; Johannes Fürnkranz; Anette Frank (SNAMS, 2018)

On Cognitive Preferences and the Interpretability of Rule-based Models. - Johannes Fürnkranz; Tomás Kliegr; Heiko Paulheim (CoRR, 2018)

Learning Interpretable Rules for Multi-label Classification. - Eneldo Loza Mencía; Johannes Fürnkranz; Eyke Hüllermeier; Michael Rapp (CoRR, 2018)

Informed Hybrid Game Tree Search for General Video Game Playing. - Tobias Joppen; Miriam Ulrike Moneke; Nils Schröder; Christian Wirth 001; Johannes Fürnkranz (IEEE Trans. Games, 2018)

Improving the Fusion of Outbreak Detection Methods with Supervised Learning. - Moritz Kulessa; Eneldo Loza Mencía; Johannes Fürnkranz (CIBB, 2019)

Ordinal Bucketing for Game Trees using Dynamic Quantile Approximation. - Tobias Joppen; Tilman Strübig; Johannes Fürnkranz (CoG, 2019)

Learning Analogy-Preserving Sentence Embeddings for Answer Selection. - Aïssatou Diallo; Markus Zopf; Johannes Fürnkranz (CoNLL, 2019)

On the Trade-Off Between Consistency and Coverage in Multi-label Rule Learning Heuristics. - Michael Rapp; Eneldo Loza Mencía; Johannes Fürnkranz (DS, 2019)

Beta Distribution Drift Detection for Adaptive Classifiers. - Lukas Fleckenstein; Sebastian Kauschke; Johannes Fürnkranz (ESANN, 2019)

Learning Context-dependent Label Permutations for Multi-label Classification. - Jinseok Nam; Young-Bum Kim; Eneldo Loza Mencía; Sunghyun Park; Ruhi Sarikaya; Johannes Fürnkranz (ICML, 2019)

Mending is Better than Ending: Adapting Immutable Classifiers to Nonstationary Environments using Ensembles of Patches. - Sebastian Kauschke; Lukas Fleckenstein; Johannes Fürnkranz (IJCNN, 2019)

Patching Deep Neural Networks for Nonstationary Environments. - Sebastian Kauschke; David Hermann Lehmann; Johannes Fürnkranz (IJCNN, 2019)

Driver Information Embedding with Siamese LSTM networks. - Hien Q. Dang; Johannes Fürnkranz (IV, 2019)

Personalized Transaction Kernels for Recommendation Using MCTS. - Maryam Tavakol; Tobias Joppen; Ulf Brefeld; Johannes Fürnkranz (KI, 2019)

Improving Answer Selection with Analogy-Preserving Sentence Embeddings. - Aïssatou Diallo; Markus Zopf; Johannes Fürnkranz (LWDA, 2019)

Deep Ordinal Reinforcement Learning. - Alexander Zap; Tobias Joppen; Johannes Fürnkranz (ECML/PKDD (3), 2019)

The PRORETA 4 City Assistant System. - Julian Schwehr; Stefan Luthardt; Hien Q. Dang; Maren Henzel; Hermann Winner; Jürgen Adamy; Johannes Fürnkranz; Volker Willert; Benedikt Lattke; Maximilian Höpfl; Christoph Wannemacher (Autom., 2019)

Improving Outbreak Detection with Stacking of Statistical Surveillance Methods. - Moritz Kulessa; Eneldo Loza Mencía; Johannes Fürnkranz (CoRR, 2019)

Advances in Machine Learning for the Behavioral Sciences. - Tomás Kliegr; Stepán Bahník; Johannes Fürnkranz (CoRR, 2019)

Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy. - Michael Rapp; Eneldo Loza Mencía; Johannes Fürnkranz (CoRR, 2019)

Vertrauenswürdiges, transparentes und robustesMaschinelles Lernen. - Christian Bauckhage; Johannes Fürnkranz; Gerhard Paaß (Handbuch der Künstlichen Intelligenz, 2020)

On Aggregation in Ensembles of Multilabel Classifiers. - Vu-Linh Nguyen; Eyke Hüllermeier; Michael Rapp; Eneldo Loza Mencía; Johannes Fürnkranz (DS, 2020)

Permutation Learning via Lehmer Codes. - Aïssatou Diallo; Markus Zopf; Johannes Fürnkranz (ECAI, 2020)

Conformal Rule-Based Multi-label Classification. - Eyke Hüllermeier; Johannes Fürnkranz; Eneldo Loza Mencía (KI, 2020)

Ordinal Monte Carlo Tree Search. - Tobias Joppen; Johannes Fürnkranz (MCS@IJCAI, 2020)

Learning Gradient Boosted Multi-label Classification Rules. - Michael Rapp; Eneldo Loza Mencía; Johannes Fürnkranz; Vu-Linh Nguyen; Eyke Hüllermeier (ECML/PKDD (3), 2020)

Rule-Based Multi-label Classification: Challenges and Opportunities. - Eyke Hüllermeier; Johannes Fürnkranz; Eneldo Loza Mencía; Vu-Linh Nguyen; Michael Rapp (RuleML+RR, 2020)

Learning Structured Declarative Rule Sets - A Challenge for Deep Discrete Learning. - Johannes Fürnkranz; Eyke Hüllermeier; Eneldo Loza Mencía; Michael Rapp (CoRR, 2020)

Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data. - Johannes Czech; Moritz Willig; Alena Beyer; Kristian Kersting; Johannes Fürnkranz (Frontiers Artif. Intell., 2020)

On cognitive preferences and the plausibility of rule-based models. - Johannes Fürnkranz; Tomás Kliegr; Heiko Paulheim (Mach. Learn., 2020)

Sum-Product Networks for Early Outbreak Detection of Emerging Diseases. - Moritz Kulessa; Bennet Wittelsbach; Eneldo Loza Mencía; Johannes Fürnkranz (AIME, 2021)

Predicting Human Card Selection in Magic: The Gathering with Contextual Preference Ranking. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoG, 2021)

Some Chess-Specific Improvements for Perturbation-Based Saliency Maps. - Jessica Fritz; Johannes Fürnkranz (CoG, 2021)

Elliptical Ordinal Embedding. - Aïssatou Diallo; Johannes Fürnkranz (DS, 2021)

Revisiting Non-specific Syndromic Surveillance. - Moritz Kulessa; Eneldo Loza Mencía; Johannes Fürnkranz (IDA, 2021)

Beyond DNF: First Steps towards Deep Rule Learning. - Florian Beck; Johannes Fürnkranz (ITAT, 2021)

The Machine Reconnaissance Blind Chess Tournament of NeurIPS 2022. - Ryan W. Gardner; Gino Perrotta; Anvay Shah; Shivaram Kalyanakrishnan; Kevin A. Wang; Gregory Clark; Timo Bertram; Johannes Fürnkranz; Martin Müller 003; Brady P. Garrison; Prithviraj Dasgupta; Saeid Rezaei (NeurIPS (Competition and Demos), 2021)

Gradient-Based Label Binning in Multi-label Classification. - Michael Rapp; Eneldo Loza Mencía; Johannes Fürnkranz; Eyke Hüllermeier (ECML/PKDD (3), 2021)

Structuring Rule Sets Using Binary Decision Diagrams. - Florian Beck; Johannes Fürnkranz; Van Quoc Phuong Huynh (RuleML+RR, 2021)

A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. - Tomás Kliegr; Stepán Bahník; Johannes Fürnkranz (Artif. Intell., 2021)

A Unifying Framework and Comparative Evaluation of Statistical and Machine Learning Approaches to Non-Specific Syndromic Surveillance. - Moritz Kulessa; Eneldo Loza Mencía; Johannes Fürnkranz (Comput., 2021)

An Investigation into Mini-Batch Rule Learning. - Florian Beck; Johannes Fürnkranz (CoRR, 2021)

A Comparison of Contextual and Non-Contextual Preference Ranking for Set Addition Problems. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoRR, 2021)

Learning Ordinal Embedding from Sets. - Aïssatou Diallo; Johannes Fürnkranz (Entropy, 2021)

Correlation-Based Discovery of Disease Patterns for Syndromic Surveillance. - Michael Rapp; Moritz Kulessa; Eneldo Loza Mencía; Johannes Fürnkranz (Frontiers Big Data, 2021)

An Empirical Investigation Into Deep and Shallow Rule Learning. - Florian Beck; Johannes Fürnkranz (Frontiers Artif. Intell., 2021)

Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoG, 2022)

Incremental Update of Locally Optimal Classification Rules. - Van Quoc Phuong Huynh; Florian Beck; Johannes Fürnkranz (DS, 2022)

On the Incremental Construction of Deep Rule Theories. - Florian Beck; Johannes Fürnkranz; Van Quoc Phuong Huynh (ITAT, 2022)

Towards Deep and Interpretable Rule Learning (invited paper). - Johannes Fürnkranz (ITAT, 2022)

Unsupervised Alignment of Distributional Word Embeddings. - Aïssatou Diallo; Johannes Fürnkranz (KI, 2022)

Comparing Boosting and Bagging for Decision Trees of Rankings. - Antonella Plaia; Simona Buscemi; Johannes Fürnkranz; Eneldo Loza Mencía (J. Classif., 2022)

GausSetExpander: A Simple Approach for Entity Set Expansion. - Aïssatou Diallo; Johannes Fürnkranz (CoRR, 2022)

Quantity vs Quality: Investigating the Trade-Off between Sample Size and Label Reliability. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoRR, 2022)

A flexible class of dependence-aware multi-label loss functions. - Eyke Hüllermeier; Marcel Wever; Eneldo Loza Mencía; Johannes Fürnkranz; Michael Rapp (Mach. Learn., 2022)

Weighting Information Sets with Siamese Neural Networks in Reconnaissance Blind Chess. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoG, 2023)

Probabilistic Scoring Lists for Interpretable Machine Learning. - Jonas Hanselle; Johannes Fürnkranz; Eyke Hüllermeier (DS, 2023)

Generalizing Conjunctive and Disjunctive Rule Learning to Learning m-of-n Concepts. - Florian Beck; Johannes Fürnkranz; Van Quoc Phuong Huynh (ITAT, 2023)

Layerwise Learning of Mixed Conjunctive and Disjunctive Rule Sets. - Florian Beck; Johannes Fürnkranz; Van Quoc Phuong Huynh (RuleML+RR, 2023)

Efficient learning of large sets of locally optimal classification rules. - Van Quoc Phuong Huynh; Johannes Fürnkranz; Florian Beck (Mach. Learn., 2023)

Tree-based dynamic classifier chains. - Eneldo Loza Mencía; Moritz Kulessa; Simon Bohlender; Johannes Fürnkranz (Mach. Learn., 2023)

Learning With Generalised Card Representations for "Magic: The Gathering". - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoG, 2024)

Dynamic Time Warping for Phase Recognition in Tribological Sensor Data. - Anna-Christina Glock; Johannes Fürnkranz (DaWaK, 2024)

Learning Deep Rule Concepts as Alternating Boolean Pattern Trees. - Florian Beck; Johannes Fürnkranz; Van Quoc Phuong Huynh (DS (2), 2024)

When Characteristic Rule-based Models Should be Preferred over Discriminative Ones. - Florian Beck; Johannes Fürnkranz; Van Quoc Phuong Huynh (ITAT, 2024)

Efficiently Training Neural Networks for Imperfect Information Games by Sampling Information Sets. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (KI, 2024)

Contrastive Learning of Preferences with a Contextual InfoNCE Loss. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (CoRR, 2024)

Learning decision catalogues for situated decision making: The case of scoring systems. - Stefan Heid; Jonas Hanselle; Johannes Fürnkranz; Eyke Hüllermeier (Int. J. Approx. Reason., 2024)

Explainable and interpretable machine learning and data mining. - Martin Atzmueller; Johannes Fürnkranz; Tomás Kliegr; Ute Schmid (Data Min. Knowl. Discov., 2024)

Predictive change point detection for heterogeneous data. - Anna-Christina Glock; Florian Sobieczky; Johannes Fürnkranz; Peter Filzmoser; Martin Jech (Neural Comput. Appl., 2024)

On the efficient implementation of classification rule learning. - Michael Rapp; Johannes Fürnkranz; Eyke Hüllermeier (Adv. Data Anal. Classif., 2024)

Neural Network-Based Information Set Weighting for Playing Reconnaissance Blind Chess. - Timo Bertram; Johannes Fürnkranz; Martin Müller 003 (IEEE Trans. Games, 2024)

Extraction of Semantically Coherent Rules from Interpretable Models. - Parisa Mahya; Johannes Fürnkranz (ICAART (1), 2025)

Partial Pre-Post Code Tree: A Memory-Efficient Tree Structure for Conjunctive Rule Mining. - Van Quoc Phuong Huynh; Florian Beck; Johannes Fürnkranz (KDD (1), 2025)

Probabilistic scoring lists for interpretable machine learning. - Jonas Hanselle; Stefan Heid; Johannes Fürnkranz; Eyke Hüllermeier (Mach. Learn., 2025)

Dynamic time warping for classifying long-term trends in time series. - Anna-Christina Glock; Klaus Chmelina; Johannes Fürnkranz; Thomas Hütter (Data Knowl. Eng., 2026)