Johannes Fürnkranz

According to our database1, Johannes Fürnkranz authored at least 186 papers between 1994 and 2021.

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Bibliography

2021
Revisiting Non-Specific Syndromic Surveillance.
CoRR, 2021

Ordinal Monte Carlo Tree Search.
CoRR, 2021

2020
On cognitive preferences and the plausibility of rule-based models.
Mach. Learn., 2020

Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data.
Frontiers Artif. Intell., 2020

Learning Structured Declarative Rule Sets - A Challenge for Deep Discrete Learning.
CoRR, 2020

A Flexible Class of Dependence-aware Multi-Label Loss Functions.
CoRR, 2020

Learning Gradient Boosted Multi-label Classification Rules.
CoRR, 2020

Rule-Based Multi-label Classification: Challenges and Opportunities.
Proceedings of the Rules and Reasoning - 4th International Joint Conference, 2020

Conformal Rule-Based Multi-label Classification.
Proceedings of the KI 2020: Advances in Artificial Intelligence, 2020

Permutation Learning via Lehmer Codes.
Proceedings of the ECAI 2020 - 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain, August 29 - September 8, 2020, 2020

On Aggregation in Ensembles of Multilabel Classifiers.
Proceedings of the Discovery Science - 23rd International Conference, 2020

2019
Simplifying Random Forests: On the Trade-off between Interpretability and Accuracy.
CoRR, 2019

Advances in Machine Learning for the Behavioral Sciences.
CoRR, 2019

Improving Outbreak Detection with Stacking of Statistical Surveillance Methods.
CoRR, 2019

The PRORETA 4 City Assistant System.
Autom., 2019

Deep Ordinal Reinforcement Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Improving Answer Selection with Analogy-Preserving Sentence Embeddings.
Proceedings of the Conference on "Lernen, Wissen, Daten, Analysen", Berlin, Germany, September 30, 2019

Personalized Transaction Kernels for Recommendation Using MCTS.
Proceedings of the KI 2019: Advances in Artificial Intelligence, 2019

Driver Information Embedding with Siamese LSTM networks.
Proceedings of the 2019 IEEE Intelligent Vehicles Symposium, 2019

Patching Deep Neural Networks for Nonstationary Environments.
Proceedings of the International Joint Conference on Neural Networks, 2019

Mending is Better than Ending: Adapting Immutable Classifiers to Nonstationary Environments using Ensembles of Patches.
Proceedings of the International Joint Conference on Neural Networks, 2019

Learning Context-dependent Label Permutations for Multi-label Classification.
Proceedings of the 36th International Conference on Machine Learning, 2019

Beta Distribution Drift Detection for Adaptive Classifiers.
Proceedings of the 27th European Symposium on Artificial Neural Networks, 2019

On the Trade-Off Between Consistency and Coverage in Multi-label Rule Learning Heuristics.
Proceedings of the Discovery Science - 22nd International Conference, 2019

Learning Analogy-Preserving Sentence Embeddings for Answer Selection.
Proceedings of the 23rd Conference on Computational Natural Language Learning, 2019

Ordinal Bucketing for Game Trees using Dynamic Quantile Approximation.
Proceedings of the IEEE Conference on Games, 2019

Improving the Fusion of Outbreak Detection Methods with Supervised Learning.
Proceedings of the Computational Intelligence Methods for Bioinformatics and Biostatistics, 2019

2018
Informed Hybrid Game Tree Search for General Video Game Playing.
IEEE Trans. Games, 2018

Learning Interpretable Rules for Multi-label Classification.
CoRR, 2018

A review of possible effects of cognitive biases on interpretation of rule-based machine learning models.
CoRR, 2018

On Cognitive Preferences and the Interpretability of Rule-based Models.
CoRR, 2018

What's Important in a Text? An Extensive Evaluation of Linguistic Annotations for Summarization.
Proceedings of the Fifth International Conference on Social Networks Analysis, 2018

Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2018

Which Scores to Predict in Sentence Regression for Text Summarization?
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2018

Exploiting Maneuver Dependency for Personalization of a Driver Model.
Proceedings of the Conference "Lernen, Wissen, Daten, Analysen", 2018

Preference-Based Monte Carlo Tree Search.
Proceedings of the KI 2018: Advances in Artificial Intelligence, 2018

Using Past Maneuver Executions for Personalization of a Driver Model.
Proceedings of the 21st International Conference on Intelligent Transportation Systems, 2018

The Need for Interpretability Biases.
Proceedings of the Advances in Intelligent Data Analysis XVII, 2018

Towards Semi-Supervised Classification of Event Streams via Denoising Autoencoders.
Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018

Leveraging Reproduction-Error Representations for Multi-Instance Classification.
Proceedings of the Discovery Science - 21st International Conference, 2018

Batchwise Patching of Classifiers.
Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017
Rank Correlation.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Preference Learning.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Rule Set.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Rule Learning.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Pruning.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Machine Learning and Game Playing.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Divide-and-Conquer Learning.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Decision Tree.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Decision Stump.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Decision Lists and Decision Trees.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Decision List.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Covering Algorithm.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Classification Rule.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Class Binarization.
Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

A Survey of Preference-Based Reinforcement Learning Methods.
J. Mach. Learn. Res., 2017

Refinement and selection heuristics in subgroup discovery and classification rule learning.
Expert Syst. Appl., 2017

Multi-objective Optimisation-Based Feature Selection for Multi-label Classification.
Proceedings of the Natural Language Processing and Information Systems, 2017

Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Time-to-lane-change prediction with deep learning.
Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems, 2017

Evaluation of Different Heuristics for Accommodating Asymmetric Loss Functions in Regression.
Proceedings of the Discovery Science - 20th International Conference, 2017

Re-training Deep Neural Networks to Facilitate Boolean Concept Extraction.
Proceedings of the Discovery Science - 20th International Conference, 2017

Interactive Data Analytics for the Humanities.
Proceedings of the Computational Linguistics and Intelligent Text Processing, 2017

2016
Special Issue on Discovery Science.
Inf. Sci., 2016

Using semantic similarity for multi-label zero-shot classification of text documents.
Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Shorter Rules Are Better, Aren't They?
Proceedings of the Discovery Science - 19th International Conference, 2016

Predicting Cargo Train Failures: A Machine Learning Approach for a Lightweight Prototype.
Proceedings of the Discovery Science - 19th International Conference, 2016

Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization.
Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, 2016

Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization.
Proceedings of the COLING 2016, 2016

What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation.
Proceedings of the COLING 2016, 2016

Model-Free Preference-Based Reinforcement Learning.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

All-in Text: Learning Document, Label, and Word Representations Jointly.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015
On Learning From Game Annotations.
IEEE Trans. Comput. Intell. AI Games, 2015

Editorial.
Data Min. Knowl. Discov., 2015

On the Importance of a Hierarchy for Learning Continuous Vector Representations of a Label Space.
Proceedings of the 3rd International Conference on Learning Representations, 2015

A Brief Overview of Rule Learning.
Proceedings of the Rule Technologies: Foundations, Tools, and Applications, 2015

Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Event-Based Clustering for Reducing Labeling Costs of Event-related Microposts.
Proceedings of the Ninth International Conference on Web and Social Media, 2015

Event-based Clustering for Reducing Labeling Costs of Incident-Related Microposts.
Proceedings of the 2nd International Workshop on Mining Urban Data co-located with 32nd International Conference on Machine Learning (ICML 2015), 2015

2014
Efficient implementation of class-based decomposition schemes for Naïve Bayes.
Mach. Learn., 2014

Preference Learning (Dagstuhl Seminar 14101).
Dagstuhl Reports, 2014

Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Large-Scale Multi-label Text Classification - Revisiting Neural Networks.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

Preference Learning from Annotated Game Databases.
Proceedings of the 16th LWA Workshops: KDML, 2014

Knowledge Discovery in Scientific Literature.
Proceedings of the 12th Edition of the Konvens Conference, 2014

Graded Multilabel Classification by Pairwise Comparisons.
Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

2013
Editorial: Preference learning and ranking.
Mach. Learn., 2013

Large-scale Multi-label Text Classification - Revisiting Neural Networks.
CoRR, 2013

A Policy Iteration Algorithm for Learning from Preference-Based Feedback.
Proceedings of the Advances in Intelligent Data Analysis XII, 2013

EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning.
Proceedings of the Asian Conference on Machine Learning, 2013

2012
Foundations of Rule Learning
Cognitive Technologies, Springer, ISBN: 978-3-540-75196-0, 2012

Preference-based reinforcement learning: a formal framework and a policy iteration algorithm.
Mach. Learn., 2012

Efficient prediction algorithms for binary decomposition techniques.
Data Min. Knowl. Discov., 2012

Unsupervised generation of data mining features from linked open data.
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, 2012

Multi-label LeGo - Enhancing Multi-label Classifiers with Local Patterns.
Proceedings of the Advances in Intelligent Data Analysis XI - 11th International Symposium, 2012

Error-Correcting Output Codes as a Transformation from Multi-Class to Multi-Label Prediction.
Proceedings of the Discovery Science - 15th International Conference, 2012

2011
A review and comparison of strategies for handling missing values in separate-and-conquer rule learning.
J. Intell. Inf. Syst., 2011

Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2011

Heuristic Rule-Based Regression via Dynamic Reduction to Classification.
Proceedings of the Report of the symposium "Lernen, 2011

Rule Stacking: An Approach for Compressing an Ensemble of Rule Sets into a Single Classifier.
Proceedings of the Discovery Science - 14th International Conference, 2011

Learning from Label Preferences.
Proceedings of the Discovery Science - 14th International Conference, 2011

2010
Explicit Feature Construction and Manipulation for Covering Rule Learning Algorithms.
Proceedings of the Advances in Machine Learning I: Dedicated to the Memory of Professor Ryszard S. Michalski, 2010

Preference Learning.
Proceedings of the Encyclopedia of Machine Learning, 2010

Rule Learning.
Proceedings of the Encyclopedia of Machine Learning, 2010

Pruning.
Proceedings of the Encyclopedia of Machine Learning, 2010

Machine Learning and Game Playing.
Proceedings of the Encyclopedia of Machine Learning, 2010

Decision Tree.
Proceedings of the Encyclopedia of Machine Learning, 2010

Decision Lists and Decision Trees.
Proceedings of the Encyclopedia of Machine Learning, 2010

Decision List.
Proceedings of the Encyclopedia of Machine Learning, 2010

Web Mining.
Proceedings of the Data Mining and Knowledge Discovery Handbook, 2nd ed., 2010

On exploiting hierarchical label structure with pairwise classifiers.
SIGKDD Explor., 2010

On the quest for optimal rule learning heuristics.
Mach. Learn., 2010

On predictive accuracy and risk minimization in pairwise label ranking.
J. Comput. Syst. Sci., 2010

Efficient voting prediction for pairwise multilabel classification.
Neurocomputing, 2010

Guest Editorial: Global modeling using local patterns.
Data Min. Knowl. Discov., 2010

Learning to Recognize Missing E-Mail Attachments.
Appl. Artif. Intell., 2010

Probability Estimation and Aggregation for Rule Learning.
Proceedings of the LWA 2010, 2010

Separate-and-conquer Regression.
Proceedings of the LWA 2010, 2010

Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain.
Proceedings of the Semantic Processing of Legal Texts: Where the Language of Law Meets the Law of Language, 2010

Exploiting Code Redundancies in ECOC.
Proceedings of the Discovery Science - 13th International Conference, 2010

Preference Learning and Ranking by Pairwise Comparison.
Proceedings of the Preference Learning., 2010

Preference Learning: An Introduction.
Proceedings of the Preference Learning., 2010

2009
A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning.
Proceedings of the SIAM International Conference on Data Mining, 2009

Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

Binary Decomposition Methods for Multipartite Ranking.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

On Table Extraction from Text Sources with Markups.
Proceedings of the LWA 2009: Workshop-Woche: Lernen, 2009

An Exploitative Monte-Carlo Poker Agent.
Proceedings of the KI 2009: Advances in Artificial Intelligence, 2009

An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules.
Proceedings of the Discovery Science, 12th International Conference, 2009

2008
Multilabel classification via calibrated label ranking.
Mach. Learn., 2008

Learning the Piece Values for Three Chess Variants.
J. Int. Comput. Games Assoc., 2008

Label ranking by learning pairwise preferences.
Artif. Intell., 2008

Handling Unknown and Imprecise Attribute Values in Propositional Rule Learning: A Feature-Based Approach.
Proceedings of the PRICAI 2008: Trends in Artificial Intelligence, 2008

Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

A Comparison of Techniques for Selecting and Combining Class Association Rules.
Proceedings of the LWA 2008, 2008

Pairwise learning of multilabel classifications with perceptrons.
Proceedings of the International Joint Conference on Neural Networks, 2008

An Empirical Investigation of the Trade-Off between Consistency and Coverage in Rule Learning Heuristics.
Proceedings of the Discovery Science, 11th International Conference, 2008

2007
An Evaluation of Efficient Multilabel Classification Algorithms for Large-Scale Problems in the Legal Domain.
Proceedings of the LWA 2007: Lernen - Wissen, 2007

On Meta-Learning Rule Learning Heuristics.
Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), 2007

On Pairwise Naive Bayes Classifiers.
Proceedings of the Machine Learning: ECML 2007, 2007

Efficient Pairwise Classification.
Proceedings of the Machine Learning: ECML 2007, 2007

On Minimizing the Position Error in Label Ranking.
Proceedings of the Machine Learning: ECML 2007, 2007

2006
Machine learning and games.
Mach. Learn., 2006

On Trading Off Consistency and Coverage in Inductive Rule Learning.
Proceedings of the LWA 2006: Lernen - Wissensentdeckung - Adaptivität, Hildesheim, Deutschland, October 9th-11th 2006, joint workshop event of several interest groups of the German Society for Informatics (GI) - 14th Workshop on Adaptivity and User Modeling in Interactive Systems (ABIS 2006) - Workshop Information Retrieval 2006 of the Special Interest Group Information Retrieval (FGIR 2006) - Workshop on Knowledge and Experience Management (FGWM 2006), 2006

A Unified Model for Multilabel Classification and Ranking.
Proceedings of the ECAI 2006, 17th European Conference on Artificial Intelligence, August 29, 2006

2005
ROC 'n' Rule Learning - Towards a Better Understanding of Covering Algorithms.
Mach. Learn., 2005

Preference Learning.
Künstliche Intell., 2005

On Position Error and Label Ranking through Iterated Choice.
Proceedings of the Lernen, 2005

Learning Label Preferences: Ranking Error Versus Position Error.
Proceedings of the Advances in Intelligent Data Analysis VI, 2005

Link-Local Features for Hypertext Classification.
Proceedings of the Semantics, Web and Mining, Joint International Workshops, 2005

Web Mining.
Proceedings of the Data Mining and Knowledge Discovery Handbook., 2005

2004
Modeling Rule Precision.
Proceedings of the LWA 2004: Lernen - Wissensentdeckung - Adaptivität, Berlin, 4., 2004

Ranking by pairwise comparison a note on risk minimization.
Proceedings of the IEEE International Conference on Fuzzy Systems, 2004

An Analysis of Stopping and Filtering Criteria for Rule Learning.
Proceedings of the Machine Learning: ECML 2004, 2004

From Local to Global Patterns: Evaluation Issues in Rule Learning Algorithms.
Proceedings of the Local Pattern Detection, 2004

2003
Round robin ensembles.
Intell. Data Anal., 2003

Combining Pairwise Classifiers with Stacking.
Proceedings of the Advances in Intelligent Data Analysis V, 2003

An Analysis of Rule Evaluation Metrics.
Proceedings of the Machine Learning, 2003

Pairwise Preference Learning and Ranking.
Proceedings of the Machine Learning: ECML 2003, 2003

2002
Round Robin Classification.
J. Mach. Learn. Res., 2002

Hyperlink ensembles: a case study in hypertext classification.
Inf. Fusion, 2002

User Profiling for the MELVIL Knowledge Retrieval System.
Appl. Artif. Intell., 2002

Pairwise Classification as an Ensemble Technique.
Proceedings of the Machine Learning: ECML 2002, 2002

A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning.
Proceedings of the Algorithmic Learning Theory, 13th International Conference, 2002

2001
Detecting Temporal Change in Event Sequences: An Application to Demographic Data.
Proceedings of the Principles of Data Mining and Knowledge Discovery, 2001

An Evaluation of Grading Classifiers.
Proceedings of the Advances in Intelligent Data Analysis, 4th International Conference, 2001

Round Robin Rule Learning.
Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28, 2001

2000
Searching for Patterns in Political Event Sequences: Experiments with the Keds Database.
Cybern. Syst., 2000

Learning to Use Operational Advice.
Proceedings of the ECAI 2000, 2000

1999
Report on the Machine-Learning in Game-Playing Workshop.
J. Int. Comput. Games Assoc., 1999

Separate-and-Conquer Rule Learning.
Artif. Intell. Rev., 1999

Exploiting Structural Information for Text Classification on the WWW.
Proceedings of the Advances in Intelligent Data Analysis, Third International Symposium, 1999

1998
Integrative Windowing.
J. Artif. Intell. Res., 1998

A Hypothesis on the Divergence of AI Research.
J. Int. Comput. Games Assoc., 1998

Guest Editorial: First-Order Knowledge Discovery in Databases.
Appl. Artif. Intell., 1998

1997
Pruning Algorithms for Rule Learning.
Mach. Learn., 1997

Knowledge Discovery in International Conflict Databases.
Appl. Artif. Intell., 1997

Noise-Tolerant Windowing.
Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, 1997

On Effort in AI Research: A Description Along Two Dimensions.
Proceedings of the Deep Blue Versus Kasparov: The Significance for Artificial Intelligence, 1997

More Efficient Windowing.
Proceedings of the Fourteenth National Conference on Artificial Intelligence and Ninth Innovative Applications of Artificial Intelligence Conference, 1997

1996
Machine Learning in Computer Chess: The Next Generation.
J. Int. Comput. Games Assoc., 1996

Digging for Peace: Using Machine Learning Methods for Assessing International Conflict Databases.
Proceedings of the 12th European Conference on Artificial Intelligence, 1996

1995
A Tight Integration of Pruning and Learning (Extended Abstract).
Proceedings of the Machine Learning: ECML-95, 1995

1994
A Comparison of Pruning Methods for Relational Concept Learning.
Proceedings of the Knowledge Discovery in Databases: Papers from the 1994 AAAI Workshop, 1994

Incremental Reduced Error Pruning.
Proceedings of the Machine Learning, 1994

FOSSIL: A Robust Relational Learner.
Proceedings of the Machine Learning: ECML-94, 1994

Top-Down Pruning in Relational Learning.
Proceedings of the Eleventh European Conference on Artificial Intelligence, 1994


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