| Exceptional model mining W Duivesteijn, AJ Feelders, A Knobbe Data Mining and Knowledge Discovery 30 (1), 47-98, 2016 | 124 | 2016 |
| Nearest neighbour classification with monotonicity constraints W Duivesteijn, A Feelders Joint European Conference on Machine Learning and Knowledge Discovery in …, 2008 | 100 | 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 | 70 | 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 | 49 | 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 | 40 | 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 | 36 | 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 | 28 | 2014 |
| Multilayer perceptron for label ranking G Ribeiro, W Duivesteijn, C Soares, A Knobbe International Conference on Artificial Neural Networks, 25-32, 2012 | 25 | 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 | 23 | 2012 |
| Exceptional preferences mining CR de Sá, W Duivesteijn, C Soares, A Knobbe International Conference on Discovery Science, 3-18, 2016 | 21 | 2016 |
| 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 | 18 | 2017 |
| 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 | 15 | 2015 |
| 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 | 14 | 2018 |
| 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 | 13 | 2013 |
| 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 | 10 | 2014 |
| Multi-label LeGo—enhancing multi-label classifiers with local patterns W Duivesteijn, EL Mencía, J Fürnkranz, A Knobbe International Symposium on Intelligent Data Analysis, 114-125, 2012 | 10 | 2012 |
| 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 | 8 | 2019 |
| 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 | 8 | 2018 |
| Interpretable domain adaptation via optimization over the Stiefel manifold C Pölitz, W Duivesteijn, K Morik Machine Learning 104 (2), 315-336, 2016 | 7 | 2016 |
| 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 | 4 | 2020 |
