Wouter Duivesteijn: full publication list

Back to the homepage.
Journal publications
  1. C. Rebelo de Sá, W. Duivesteijn, P. Azevedo, A.M. Jorge, C. Soares, A. Knobbe: Discovering a Taste for the Unusual - Exceptional Models for Preference Mining. In Machine Learning 107 (11), pp. 1775-1807, 2018.
    Impact factor: 1.855 (2017).
  2. L. Downar, W. Duivesteijn: Exceptionally Monotone Models --- the Rank Correlation Model Class for Exceptional Model Mining. In: Knowledge and Information Systems 51 (2), pp. 369-394, 2017.
    Impact factor: 2.247.
  3. C. Pölitz, W. Duivesteijn, K. Morik: Interpretable Domain Adaptation via Optimization over the Stiefel Manifold. In: Machine Learning 104 (2-3), pp. 315-336, 2016.
    Impact factor: 1.848.
  4. W. Duivesteijn: Correction to Jin-Ting Zhang's "Approximate and Asymptotic Distributions of Chi-Squared-Type Mixtures with Applications". In: Journal of the American Statistical Association 111 (515), pp. 1370-1371, 2016.
    Impact factor: 2.016.
  5. W. Duivesteijn, A.J. Feelders, A. Knobbe: Exceptional Model Mining - Supervised Descriptive Local Pattern Mining with Complex Target Concepts. In: Data Mining and Knowledge Discovery 30 (1), pp. 47-98, 2016.
    Impact factor: 3.160.
  6. R.M. Konijn, W. Duivesteijn, M. Meeng, A. Knobbe: Cost-based Quality Measures in Subgroup Discovery. In: Journal of Intelligent Information Systems, 45 (3), pp. 337-355, 2015.
    Impact factor: 1.000.
  7. P. Lohuis, S. Faraj-Hakim, W. Duivesteijn, A. Knobbe, A.-J. Tasman: Benefits of a Short, Practical Questionnaire to Measure Subjective Perception of Nasal Appearance after Aesthetic Rhinoplasty. In: Plastic and Reconstructive Surgery 132 (6), pp. 913e-923e, 2013.
    Impact factor: 3.328.
  8. P.J.F.M. Lohuis, S. Hakim, A. Knobbe, W. Duivesteijn, G.M. Bran: 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 aesthetic rhinoplasty patients. In: Archives of Facial Plastic Surgery 14 (5), pp. 346-353, 2012.
    Impact factor: 1.463.
  9. S. Hakim, A. Knobbe, W. Duivesteijn, P.J.F.M. Lohuis: Results of a screening questionnaire measuring physical perception of patients undergoing esthetic rhinoplasty: a statistical analysis. In: Nederlands Tijdschrift voor Keel-Neus-Oorheelkunde (Dutch Journal for Otorhinolaryngology) (2), p. 100, 2010.
    Impact factor: 0.
Conference publications
  1. A. Belfodil, W. Duivesteijn, M. Plantevit, S. Cazalens, P. Lamarre: DEvIANT: Discovering significant exceptional (dis-)agreement within groups. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2019, to appear.
    Acceptance rate: 0.1771 (130 out of 734).
  2. S. Hess, W. Duivesteijn: k is the Magic Number - Inferring the Number of Clusters Through Nonparametric Concentration Inequalities. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2019, to appear.
    Acceptance rate: 0.1771 (130 out of 734).
  3. S. Hess, W. Duivesteijn, P.-J. Honysz, K. Morik: The SpectACl of Nonconvex Clustering: A Spectral Approach to Density-Based Clustering. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019), to appear.
    Acceptance rate: 0.1621 (1150 out of 7095).
  4. X. Du, W. Duivesteijn, M. Klabbers, M. Pechenizkiy: ELBA: Exceptional Learning Behavior Analysis. In: Proceedings of the Eleventh International Conference on Educational Data Mining (EDM 2018), pp. 312-318, 2018.
    Acceptance rate: 0.4207 (61 out of 145).
  5. J. Lijffijt, B. Kang, W. Duivesteijn, K. Puolamäki, E. Oikarinen, T. De Bie: Subjectively Interesting Subgroup Discovery on Real-valued Targets. In: Proceedings of the 34th IEEE International Conference on Data Engineering (ICDE 2018), pp. 1352-1355, 2018.
    Acceptance rate: 0.2311 (98 out of 424).
    A substantially longer version appeared on arXiv.
  6. W. Duivesteijn, T. Farzami, T. Putman, E. Peer, H.J.P. Weerts, J.N. Adegeest, G. Foks, M. Pechenizkiy: Have It Both Ways - from A/B Testing to A&B Testing with Exceptional Model Mining. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017), part III, pp. 114-126, 2017.
    Acceptance rate: 0.2903 (27 out of 93).
  7. C. Rebelo de Sá, W. Duivesteijn, C. Soares, A. Knobbe: Exceptional Preferences Mining. In: Proceedings of the 19th International Conference on Discovery Science (DS 2016), pp. 3-18, 2016.
    Acceptance rate: 0.5000 (30 out of 60).
  8. L. Downar, W. Duivesteijn: Exceptionally Monotone Models - the Rank Correlation Model Class for Exceptional Model Mining. In: Proceedings of the 15th IEEE International Conference on Data Mining (ICDM 2015), pp. 111-120, 2015.
    Acceptance rate: 0.0843 (68 out of 807). Including short papers: 0.1821 (147 out of 807).
    A substantially longer version appeared as a Bachelor Thesis at the TU Dortmund.
  9. W. Duivesteijn, J. Thaele: Understanding Where Your Classifier Does (Not) Work. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015) (III), pp. 250-253, 2015.
    Acceptance rate: 0.4828 (14 out of 29).
  10. W. Duivesteijn, J. Thaele: Understanding Where Your Classifier Does (Not) Work - the SCaPE Model Class for EMM. In: Proceedings of the 14th IEEE International Conference on Data Mining (ICDM 2014), pp. 809--814, 2014.
    Acceptance rate: 0.1953 (142 out of 727).
    A substantially longer version appeared as a Technical Report of the SFB 876 at the TU Dortmund.
  11. J. Witteveen, W. Duivesteijn, A. Knobbe, P. Grünwald: RealKRIMP - Finding Hyperintervals that Compress with MDL for Real-Valued Data. In: Proceedings of the 13th International Symposium on Intelligent Data Analysis (IDA 2014), pp. 368--379, 2014.
    Acceptance rate: 0.4800 (36 out of 75).
    A substantially longer version appeared as a Bachelor thesis at the Universiteit Leiden.
  12. M. Meeng, W. Duivesteijn, A. Knobbe: ROCsearch - An ROC-guided Search Strategy for Subgroup Discovery. In: Proceedings of the 2014 SIAM International Conference on Data Mining (SDM 2014), pp. 704-712, 2014.
    Acceptance rate: 0.3084 (120 out of 389).
  13. W. Duivesteijn, A. Knobbe: Exceptional Model Mining - Describing Deviations in Datasets. In: Proceedings of the 22nd Belgian-Dutch Conference on Machine Learning (BENELEARN 2013), p. 86, 2013.
    Acceptance rate: 0.8919 (33 out of 37).
  14. R.M. Konijn, W. Duivesteijn, W. Kowalczyk, A. Knobbe: Discovering Local Subgroups, with an Application to Fraud Detection. In: Proceedings of the 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), pp. 1-12, 2013.
    Acceptance rate: 0.1134 (39 out of 344). Including short presentations: 0.2849 (98 out of 344).
  15. W. Duivesteijn, E. Loza Mencía, J. Fürnkranz, A. Knobbe: Multi-label LeGo - Enhancing Multi-label Classifiers with Local Patterns. In: Proceedings of the 11th International Symposium on Intelligent Data Analysis (IDA 2012), pp. 114-125, 2012.
    Acceptance rate: 0.2250 (18 out of 80). Including poster presentations: 0.4375 (35 out of 80).
    A substantially longer version appeared as a Technical Report of the TU Darmstadt, TUD-KE-2012-02.
  16. G. Ribeiro, W. Duivesteijn, C. Soares, A. Knobbe: Multilayer Perceptron for Label Ranking. In: Proceedings of the 22nd International Conference on Artificial Neural Networks (ICANN 2012), pp. 25-32, 2012.
    Acceptance rate: 0.6559 (162 out of 247).
  17. W. Duivesteijn, A.J. Feelders, A. Knobbe: Different Slopes for Different Folks - Mining for Exceptional Regression Models with Cook's Distance. In: Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2012), pp. 868-876, 2012.
    Acceptance rate: 0.1762 (133 out of 755).
  18. W. Duivesteijn, A. Knobbe: Exploiting False Discoveries - Statistical Validation of Patterns and Quality Measures in Subgroup Discovery. In: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM 2011), pp. 151-160, 2011.
    Acceptance rate: 0.1285 (101 out of 786). Including short papers: 0.1883 (148 out of 786).
  19. W. Duivesteijn, A. Knobbe, A.J. Feelders, M. van Leeuwen: Subgroup Discovery meets Bayesian networks - an Exceptional Model Mining approach. In: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM 2010), pp. 158-167, 2010.
    Acceptance rate: 0.0903 (72 out of 797). Including short papers: 0.1945 (155 out of 797).
  20. W. Duivesteijn, A.J. Feelders: Nearest Neighbour Classification with Monotonicity Constraints. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2008 (I), pp. 301-316, 2008.
    Acceptance rate: 0.1919 (100 out of 521).
Workshop publications
  1. S.B. van der Zon, W. Duivesteijn, W. van Ipenburg, J. Veldsink, M. Pechenizkiy: ICIE 1.0: A Novel Tool for Interactive Contextual Interaction Explanations. In: MIDAS 2018, PAP 2018: ECML PKDD 2018 Workshops, pp. 81-94, 2019.
  2. S.B. van der Zon, O. Zeev Ben Mordehai, T. Vrijdag, W. van Ipenburg, J. Veldsink, W. Duivesteijn, M. Pechenizkiy: BoostEMM - Transparent Boosting using Exceptional Model Mining. In: Proceedings of the Second Workshop on MIning DAta for financial applicationS (MIDAS 2017), pp. 5-14, 2017.
  3. W. Duivesteijn, M. Meeng, A. Knobbe: ROCsearch in a Wider Context - A ROC-Guided Search Strategy for Subgroup Discovery and Beyond. In: Proceedings of the First International Workshop on Learning over Multiple Contexts (LMCE 2014), 2014.
  4. W. Duivesteijn: A Short Survey of Exceptional Model Mining - Exploring Unusual Interactions Between Multiple Targets. In: Proceedings of the 2014 International Workshop on Multi-Target Prediction (MTP 2014), 2014.
  5. M. Meeng, W. Duivesteijn, A. Knobbe: ROCsearch - An ROC-guided Search Strategy for Subgroup Discovery. In: Proceedings of the 2014 Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML 2014), p. 180, 2014.
  6. R.M. Konijn, W. Duivesteijn, M. Meeng, A. Knobbe: Cost-based Quality Measures in Subgroup Discovery. In: Proceedings of the 3rd Quality Issues, Measures of Interestingness, and Evaluation of data mining models workshop (QIMIE 2013), PAKDD Workshops, pp. 404-415, 2013.
Back to the homepage.