Wouter Duivesteijn
Wouter Duivesteijn
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Cited by
Cited by
Exceptional model mining
W Duivesteijn, AJ Feelders, A Knobbe
Data Mining and Knowledge Discovery 30 (1), 47-98, 2016
Nearest neighbour classification with monotonicity constraints
W Duivesteijn, A Feelders
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2008
Subgroup discovery meets Bayesian networks--an exceptional model mining approach
W Duivesteijn, A Knobbe, A Feelders, M van Leeuwen
2010 IEEE International Conference on Data Mining, 158-167, 2010
Exploiting false discoveries--statistical validation of patterns and quality measures in subgroup discovery
W Duivesteijn, A Knobbe
2011 IEEE 11th International Conference on Data Mining, 151-160, 2011
Benefits of a short, practical questionnaire to measure subjective perception of nasal appearance after aesthetic rhinoplasty
PJFM Lohuis, S Hakim, W Duivesteijn, A Knobbe, AJ Tasman
Plastic and Reconstructive Surgery 132 (6), 913e-923e, 2013
Different slopes for different folks: mining for exceptional regression models with cook's distance
W Duivesteijn, A Feelders, A Knobbe
Proceedings of the 18th ACM SIGKDD international conference on Knowledge …, 2012
Understanding where your classifier does (not) work--the SCaPE model class for EMM
W Duivesteijn, J Thaele
2014 IEEE International Conference on Data Mining, 809-814, 2014
Multilayer perceptron for label ranking
G Ribeiro, W Duivesteijn, C Soares, A Knobbe
International Conference on Artificial Neural Networks, 25-32, 2012
Split hump technique for reduction of the overprojected nasal dorsum: a statistical analysis on subjective body image in relation to nasal appearance and nasal patency in 97 …
PJFM Lohuis, S Faraj-Hakim, A Knobbe, W Duivesteijn, GM Bran
Archives of Facial Plastic Surgery 14 (5), 346-353, 2012
Exceptional preferences mining
C Rebelo de Sá, W Duivesteijn, C Soares, A Knobbe
International Conference on Discovery Science, 3-18, 2016
The SpectACl of nonconvex clustering: a spectral approach to density-based clustering
S Hess, W Duivesteijn, P Honysz, K Morik
Proceedings of the AAAI conference on artificial intelligence 33 (01), 3788-3795, 2019
Exceptionally monotone models—the rank correlation model class for exceptional model mining
L Downar, W Duivesteijn
Knowledge and Information Systems 51 (2), 369-394, 2017
Discovering a taste for the unusual: exceptional models for preference mining
CR de Sá, W Duivesteijn, P Azevedo, AM Jorge, C Soares, A Knobbe
Machine Learning 107 (11), 1775-1807, 2018
Adversarial balancing-based representation learning for causal effect inference with observational data
X Du, L Sun, W Duivesteijn, A Nikolaev, M Pechenizkiy
Data Mining and Knowledge Discovery 35 (4), 1713-1738, 2021
Subjectively interesting subgroup discovery on real-valued targets
J Lijffijt, B Kang, W Duivesteijn, K Puolamaki, E Oikarinen, T De Bie
2018 IEEE 34th International Conference on Data Engineering (ICDE), 1352-1355, 2018
Cost-based quality measures in subgroup discovery
RM Konijn, W Duivesteijn, M Meeng, A Knobbe
Journal of Intelligent Information Systems 45 (3), 337-355, 2015
Discovering local subgroups, with an application to fraud detection
RM Konijn, W Duivesteijn, W Kowalczyk, A Knobbe
Pacific-Asia Conference on Knowledge Discovery and Data Mining, 1-12, 2013
Softmax-based classification is k-means clustering: Formal proof, consequences for adversarial attacks, and improvement through centroid based tailoring
S Hess, W Duivesteijn, D Mocanu
arXiv preprint arXiv:2001.01987, 2020
ROCsearch — An ROC-guided Search Strategy for Subgroup Discovery
M Meeng, W Duivesteijn, A Knobbe
Proceedings of the 2014 SIAM International Conference on Data Mining, 704-712, 2014
Multi-label LeGo—enhancing multi-label classifiers with local patterns
W Duivesteijn, E Loza Mencía, J Fürnkranz, A Knobbe
International Symposium on Intelligent Data Analysis, 114-125, 2012
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