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2020 – today
- 2024
[c32]Iko Vloothuis, Wouter Duivesteijn:
RMI-RRG: A Soft Protocol to Postulate Monotonicity Constraints for Tabular Datasets. IDA (1) 2024: 16-27- [c31]Rianne Margaretha Schouten, Wouter Duivesteijn, Pekka Räsänen, Jacob M. Paul, Mykola Pechenizkiy:
Exceptional Subitizing Patterns: Exploring Mathematical Abilities of Finnish Primary School Children with Piecewise Linear Regression. ECML/PKDD (10) 2024: 66-82 - 2022
- [j10]Rianne Margaretha Schouten
, Marcos L. P. Bueno, Wouter Duivesteijn, Mykola Pechenizkiy:
Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions. Data Min. Knowl. Discov. 36(1): 379-413 (2022) - [c30]Ruben Franciscus Adrianus Verhaegh, Jacco Johannes Egbert Kiezebrink, Frank Nusteling, Arnaud Wander André Rio, Márton Bendegúz Bendicsek, Wouter Duivesteijn, Rianne Margaretha Schouten:
A Clustering-Inspired Quality Measure for Exceptional Preferences Mining - Design Choices and Consequences. DS 2022: 429-444 - [c29]Joost F. van der Haar, Sander C. Nagelkerken, Igor G. Smit, Kjell van Straaten, Janneke A. Tack, Rianne Margaretha Schouten, Wouter Duivesteijn:
Efficient Subgroup Discovery Through Auto-Encoding. IDA 2022: 327-340 - [c28]Rianne Margaretha Schouten, Wouter Duivesteijn, Mykola Pechenizkiy:
Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS). SDM 2022: 585-593 - 2021
- [j9]Xin Du, Lei Sun, Wouter Duivesteijn, Alexander G. Nikolaev, Mykola Pechenizkiy:
Adversarial balancing-based representation learning for causal effect inference with observational data. Data Min. Knowl. Discov. 35(4): 1713-1738 (2021) - [c27]Wouter Duivesteijn, Thomas C. van Dijk:
Exceptional Gestalt Mining: Combining Magic Cards to Make Complex Coalitions Thrive. MLSA@PKDD/ECML 2021: 191-204 - [i7]Xin Du, Subramanian Ramamoorthy, Wouter Duivesteijn, Jin Tian, Mykola Pechenizkiy:
Beyond Discriminant Patterns: On the Robustness of Decision Rule Ensembles. CoRR abs/2109.10432 (2021) - 2020
- [j8]José María Luna, Mykola Pechenizkiy, Wouter Duivesteijn, Sebastián Ventura:
Exceptional in so Many Ways - Discovering Descriptors That Display Exceptional Behavior on Contrasting Scenarios. IEEE Access 8: 200982-200994 (2020) - [j7]Xin Du, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy:
Exceptional spatio-temporal behavior mining through Bayesian non-parametric modeling. Data Min. Knowl. Discov. 34(5): 1267-1290 (2020) - [j6]Wouter Duivesteijn, Sibylle Hess, Xin Du:
How to cheat the page limit. WIREs Data Mining Knowl. Discov. 10(3) (2020) - [c26]Xin Du, Yulong Pei, Wouter Duivesteijn, Mykola Pechenizkiy:
Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data. AAAI 2020: 3809-3816 - [c25]Youri Soons, Remco M. Dijkman, Maurice Jilderda, Wouter Duivesteijn:
Predicting Remaining Useful Life with Similarity-Based Priors. IDA 2020: 483-495 - [i6]Sibylle Hess, Wouter Duivesteijn, Decebal Constantin Mocanu:
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring. CoRR abs/2001.01987 (2020)
2010 – 2019
- 2019
- [c24]Sibylle Hess, Wouter Duivesteijn, Philipp Honysz, Katharina Morik:
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. AAAI 2019: 3788-3795 - [c23]Adnene Belfodil, Wouter Duivesteijn, Marc Plantevit, Sylvie Cazalens, Philippe Lamarre:
DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups. ECML/PKDD (1) 2019: 3-20 - [c22]Sibylle Hess, Wouter Duivesteijn:
k Is the Magic Number - Inferring the Number of Clusters Through Nonparametric Concentration Inequalities. ECML/PKDD (1) 2019: 257-273 - [e2]Martin Atzmueller, Wouter Duivesteijn:
Artificial Intelligence - 30th Benelux Conference, BNAIC 2018, 's-Hertogenbosch, The Netherlands, November 8-9, 2018, Revised Selected Papers. Communications in Computer and Information Science 1021, Springer 2019, ISBN 978-3-030-31977-9 [contents] - [i5]Xin Du, Lei Sun, Wouter Duivesteijn, Alexander G. Nikolaev, Mykola Pechenizkiy:
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data. CoRR abs/1904.13335 (2019) - [i4]Sibylle Hess, Wouter Duivesteijn, Philipp Honysz, Katharina Morik:
The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. CoRR abs/1907.00680 (2019) - [i3]Sibylle Hess, Wouter Duivesteijn:
k is the Magic Number - Inferring the Number of Clusters Through Nonparametric Concentration Inequalities. CoRR abs/1907.02343 (2019) - 2018
- [j5]Cláudio Rebelo de Sá, Wouter Duivesteijn, Paulo J. Azevedo, Alípio Mário Jorge, Carlos Soares, Arno J. Knobbe:
Discovering a taste for the unusual: exceptional models for preference mining. Mach. Learn. 107(11): 1775-1807 (2018) - [c21]Xin Du, Wouter Duivesteijn, Mykola Pechenizkiy:
ELBA: Exceptional Learning Behavior Analysis. EDM 2018 - [c20]Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie:
Subjectively Interesting Subgroup Discovery on Real-Valued Targets. ICDE 2018: 1352-1355 - [c19]Simon van der Zon, Wouter Duivesteijn, Werner van Ipenburg, Jan Veldsink, Mykola Pechenizkiy:
ICIE 1.0: A Novel Tool for Interactive Contextual Interaction Explanations. MIDAS/PAP@PKDD/ECML 2018: 81-94 - [e1]Wouter Duivesteijn, Arno Siebes, Antti Ukkonen:
Advances in Intelligent Data Analysis XVII - 17th International Symposium, IDA 2018, 's-Hertogenbosch, The Netherlands, October 24-26, 2018, Proceedings. Lecture Notes in Computer Science 11191, Springer 2018, ISBN 978-3-030-01767-5 [contents] - [i2]Oren Zeev-Ben-Mordehai, Wouter Duivesteijn, Mykola Pechenizkiy:
Controversy Rules - Discovering Regions Where Classifiers (Dis-)Agree Exceptionally. CoRR abs/1808.07243 (2018) - 2017
- [j4]Lennart Downar, Wouter Duivesteijn:
Exceptionally monotone models - the rank correlation model class for Exceptional Model Mining. Knowl. Inf. Syst. 51(2): 369-394 (2017) - [c18]Simon van der Zon, Oren Zeev-Ben-Mordehai, Tom Vrijdag, Werner van Ipenburg, Wouter Duivesteijn, Jan Veldsink, Mykola Pechenizkiy:
BoostEMM - Transparent Boosting using Exceptional Model Mining. MIDAS@PKDD/ECML 2017: 5-16 - [c17]Wouter Duivesteijn, Tara Farzami, Thijs Putman, Evertjan Peer, Hilde J. P. Weerts, Jasper N. Adegeest, Gerson Foks, Mykola Pechenizkiy:
Have It Both Ways - From A/B Testing to A&B Testing with Exceptional Model Mining. ECML/PKDD (3) 2017: 114-126 - [i1]Jefrey Lijffijt, Bo Kang, Wouter Duivesteijn, Kai Puolamäki, Emilia Oikarinen, Tijl De Bie:
Subjectively Interesting Subgroup Discovery on Real-valued Targets. CoRR abs/1710.04521 (2017) - 2016
- [j3]Wouter Duivesteijn, Ad Feelders, Arno J. Knobbe:
Exceptional Model Mining - Supervised descriptive local pattern mining with complex target concepts. Data Min. Knowl. Discov. 30(1): 47-98 (2016) - [j2]Christian Pölitz, Wouter Duivesteijn, Katharina Morik:
Interpretable domain adaptation via optimization over the Stiefel manifold. Mach. Learn. 104(2-3): 315-336 (2016) - [c16]Wouter Duivesteijn, Marvin Meeng:
SCHEP - A Geometric Quality Measure for Regression Rule Sets, Gauging Ranking Consistency Throughout the Real-Valued Target Space. Solving Large Scale Learning Tasks 2016: 272-285 - [c15]Cláudio Rebelo de Sá, Wouter Duivesteijn, Carlos Soares, Arno J. Knobbe:
Exceptional Preferences Mining. DS 2016: 3-18 - 2015
- [j1]Rob M. Konijn, Wouter Duivesteijn, Marvin Meeng, Arno J. Knobbe:
Cost-based quality measures in subgroup discovery. J. Intell. Inf. Syst. 45(3): 337-355 (2015) - [c14]Lennart Downar, Wouter Duivesteijn:
Exceptionally Monotone Models - The Rank Correlation Model Class for Exceptional Model Mining. ICDM 2015: 111-120 - [c13]Wouter Duivesteijn, Julia Thaele:
Understanding Where Your Classifier Does (Not) Work. ECML/PKDD (3) 2015: 250-253 - 2014
- [c12]Wouter Duivesteijn, Julia Thaele:
Understanding Where Your Classifier Does (Not) Work - The SCaPE Model Class for EMM. ICDM 2014: 809-814 - [c11]Jouke Witteveen, Wouter Duivesteijn, Arno J. Knobbe, Peter Grünwald:
RealKrimp - Finding Hyperintervals that Compress with MDL for Real-Valued Data. IDA 2014: 368-379 - [c10]Marvin Meeng, Wouter Duivesteijn, Arno J. Knobbe:
ROCsearch - An ROC-Guided Search Strategy for Subgroup Discovery. LWA 2014: 180 - [c9]Marvin Meeng, Wouter Duivesteijn, Arno J. Knobbe:
ROCsearch - An ROC-guided Search Strategy for Subgroup Discovery. SDM 2014: 704-712 - 2013
- [c8]Rob M. Konijn, Wouter Duivesteijn, Wojtek Kowalczyk, Arno J. Knobbe:
Discovering Local Subgroups, with an Application to Fraud Detection. PAKDD (1) 2013: 1-12 - [c7]Rob M. Konijn, Wouter Duivesteijn, Marvin Meeng, Arno J. Knobbe:
Cost-Based Quality Measures in Subgroup Discovery. PAKDD Workshops 2013: 404-415 - 2012
- [c6]Geraldina Ribeiro, Wouter Duivesteijn, Carlos Soares, Arno J. Knobbe:
Multilayer Perceptron for Label Ranking. ICANN (2) 2012: 25-32 - [c5]Wouter Duivesteijn, Eneldo Loza Mencía, Johannes Fürnkranz, Arno J. Knobbe:
Multi-label LeGo - Enhancing Multi-label Classifiers with Local Patterns. IDA 2012: 114-125 - [c4]Wouter Duivesteijn, Ad Feelders, Arno J. Knobbe:
Different slopes for different folks: mining for exceptional regression models with cook's distance. KDD 2012: 868-876 - 2011
- [c3]Wouter Duivesteijn, Arno J. Knobbe:
Exploiting False Discoveries - Statistical Validation of Patterns and Quality Measures in Subgroup Discovery. ICDM 2011: 151-160 - 2010
- [c2]Wouter Duivesteijn, Arno J. Knobbe, Ad Feelders, Matthijs van Leeuwen:
Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach. ICDM 2010: 158-167
2000 – 2009
- 2008
- [c1]Wouter Duivesteijn, Ad Feelders:
Nearest Neighbour Classification with Monotonicity Constraints. ECML/PKDD (1) 2008: 301-316
Coauthor Index
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last updated on 2024-10-07 21:19 CEST by the dblp team
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