| Probabilistic scoring lists for interpretable machine learning.   | MLJ | 2025 | 0 | 
        
        
            | Explainable and interpretable machine learning and data mining.   | DMKD | 2024 | 0 | 
        
        
            | Efficient learning of large sets of locally optimal classification rules.   | MLJ | 2023 | 1 | 
        
        
            | Tree-based dynamic classifier chains.   | MLJ | 2023 | 0 | 
        
        
            | A flexible class of dependence-aware multi-label loss functions.   | MLJ | 2022 | 0 | 
        
        
            | Gradient-Based Label Binning in Multi-label Classification.   | ECML/PKDD | 2021 | 2 | 
        
        
            | A review of possible effects of cognitive biases on interpretation of rule-based machine learning models.   | Artificial Intelligence | 2021 | 0 | 
        
        
            | Learning Gradient Boosted Multi-label Classification Rules.   | ECML/PKDD | 2020 | 19 | 
        
        
            | Permutation Learning via Lehmer Codes.   | ECAI | 2020 | 5 | 
        
        
            | On cognitive preferences and the plausibility of rule-based models.   | MLJ | 2020 | 0 | 
        
        
            | Learning Analogy-Preserving Sentence Embeddings for Answer Selection.   | CoNLL | 2019 | 7 | 
        
        
            | Mending is Better than Ending: Adapting Immutable Classifiers to Nonstationary Environments using Ensembles of Patches.   | IJCNN | 2019 | 0 | 
        
        
            | Deep Ordinal Reinforcement Learning.   | ECML/PKDD | 2019 | 5 | 
        
        
            | Learning Context-dependent Label Permutations for Multi-label Classification.   | ICML | 2019 | 11 | 
        
        
            | Patching Deep Neural Networks for Nonstationary Environments.   | IJCNN | 2019 | 2 | 
        
        
            | Beta Distribution Drift Detection for Adaptive Classifiers.   | ESANN | 2019 | 0 | 
        
        
            | Batchwise Patching of Classifiers.   | AAAI | 2018 | 21 | 
        
        
            | Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules.   | PAKDD | 2018 | 6 | 
        
        
            | Maximizing Subset Accuracy with Recurrent Neural Networks in Multi-label Classification.   | NIPS/NeurIPS | 2017 | 131 | 
        
        
            | A Survey of Preference-Based Reinforcement Learning Methods.   | JMLR | 2017 | 0 | 
        
        
            | Using semantic similarity for multi-label zero-shot classification of text documents.   | ESANN | 2016 | 31 | 
        
        
            | Model-Free Preference-Based Reinforcement Learning.   | AAAI | 2016 | 72 | 
        
        
            | All-in Text: Learning Document, Label, and Word Representations Jointly.   | AAAI | 2016 | 54 | 
        
        
            | Beyond Centrality and Structural Features: Learning Information Importance for Text Summarization.   | CoNLL | 2016 | 15 | 
        
        
            | What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation.   | COLING | 2016 | 4 | 
        
        
            | Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization.   | COLING | 2016 | 11 | 
        
        
            | Editorial.   | DMKD | 2015 | 0 | 
        
        
            | Predicting Unseen Labels Using Label Hierarchies in Large-Scale Multi-label Learning.   | ECML/PKDD | 2015 | 14 | 
        
        
            | Separating Rule Refinement and Rule Selection Heuristics in Inductive Rule Learning.   | ECML/PKDD | 2014 | 15 | 
        
        
            | Graded Multilabel Classification by Pairwise Comparisons.   | ICDM | 2014 | 17 | 
        
        
            | Efficient implementation of class-based decomposition schemes for Naïve Bayes.   | MLJ | 2014 | 0 | 
        
        
            | Large-Scale Multi-label Text Classification - Revisiting Neural Networks.   | ECML/PKDD | 2014 | 0 | 
        
        
            | EPMC: Every Visit Preference Monte Carlo for Reinforcement Learning.   | ACML | 2013 | 13 | 
        
        
            | Editorial: Preference learning and ranking.   | MLJ | 2013 | 8 | 
        
        
            | Preference-based reinforcement learning: a formal framework and a policy iteration algorithm.   | MLJ | 2012 | 107 | 
        
        
            | Efficient prediction algorithms for binary decomposition techniques.   | DMKD | 2012 | 0 | 
        
        
            | Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning.   | ECML/PKDD | 2011 | 42 | 
        
        
            | Heuristic Rule-Based Regression via Dynamic Reduction to Classification.   | IJCAI | 2011 | 25 | 
        
        
            | On the quest for optimal rule learning heuristics.   | MLJ | 2010 | 76 | 
        
        
            | Guest Editorial: Global modeling using local patterns.   | DMKD | 2010 | 16 | 
        
        
            | Binary Decomposition Methods for Multipartite Ranking.   | ECML/PKDD | 2009 | 79 | 
        
        
            | Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification.   | ECML/PKDD | 2009 | 11 | 
        
        
            | A Re-evaluation of the Over-Searching Phenomenon in Inductive Rule Learning.   | SDM | 2009 | 0 | 
        
        
            | Efficient voting prediction for pairwise multilabel classification.   | ESANN | 2009 | 0 | 
        
        
            | Pairwise learning of multilabel classifications with perceptrons.   | IJCNN | 2008 | 39 | 
        
        
            | Label ranking by learning pairwise preferences.   | Artificial Intelligence | 2008 | 533 | 
        
        
            | Multilabel classification via calibrated label ranking.   | MLJ | 2008 | 785 | 
        
        
            | Efficient Pairwise Multilabel Classification for Large-Scale Problems in the Legal Domain.   | ECML/PKDD | 2008 | 157 | 
        
        
            | On Meta-Learning Rule Learning Heuristics.   | ICDM | 2007 | 13 | 
        
        
            | On Pairwise Naive Bayes Classifiers.   | ECML/PKDD | 2007 | 33 | 
        
        
            | On Minimizing the Position Error in Label Ranking.   | ECML/PKDD | 2007 | 15 | 
        
        
            | Efficient Pairwise Classification.   | ECML/PKDD | 2007 | 109 | 
        
        
            | A Unified Model for Multilabel Classification and Ranking.   | ECAI | 2006 | 124 | 
        
        
            | Machine learning and games.   | MLJ | 2006 | 53 | 
        
        
            | ROC 'n' Rule Learning - Towards a Better Understanding of Covering Algorithms.   | MLJ | 2005 | 0 | 
        
        
            | An Analysis of Stopping and Filtering Criteria for Rule Learning.   | ECML/PKDD | 2004 | 15 | 
        
        
            | An Analysis of Rule Evaluation Metrics.   | ICML | 2003 | 114 | 
        
        
            | Pairwise Preference Learning and Ranking.   | ECML/PKDD | 2003 | 235 | 
        
        
            | Round Robin Classification.   | JMLR | 2002 | 490 | 
        
        
            | Pairwise Classification as an Ensemble Technique.   | ECML/PKDD | 2002 | 53 | 
        
        
            | Detecting Temporal Change in Event Sequences: An Application to Demographic Data.   | ECML/PKDD | 2001 | 27 | 
        
        
            | Round Robin Rule Learning.   | ICML | 2001 | 71 | 
        
        
            | Learning to Use Operational Advice.   | ECAI | 2000 | 5 | 
        
        
            | Integrative Windowing.   | JAIR | 1998 | 61 | 
        
        
            | Pruning Algorithms for Rule Learning.   | MLJ | 1997 | 128 | 
        
        
            | Noise-Tolerant Windowing.   | IJCAI | 1997 | 10 | 
        
        
            | Digging for Peace: Using Machine Learning Methods for Assessing International Conflict Databases.   | ECAI | 1996 | 13 | 
        
        
            | A Tight Integration of Pruning and Learning (Extended Abstract).   | ECML/PKDD | 1995 | 6 | 
        
        
            | Top-Down Pruning in Relational Learning.   | ECAI | 1994 | 17 | 
        
        
            | Incremental Reduced Error Pruning.   | ICML | 1994 | 444 | 
        
        
            | FOSSIL: A Robust Relational Learner.   | ECML/PKDD | 1994 | 49 |