Wouter Duivesteijn: full publication list

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Journal publications
  1. P.J.A.M. Mulders, E.R. van den Heuvel, P. Reidsma, W. Duivesteijn: Introducting exceptional growth mining - Analyzing the impact of soil characteristics on on-farm crop growth and yield variability. In: PLOS ONE 19(1):e0296684, 2024.
    Impact factor: 3.7 (2022).
  2. R.M. Schouten, M.L.P. Bueno, W. Duivesteijn, M. Pechenizkiy: Mining Sequences with Exceptional Transition Behaviour of Varying Order using Quality Measures based on Information-Theoretic Scoring Functions. In: Data Mining and Knowledge Discovery 36(1), pp. 379-413, 2022.
    Impact factor: 4.8.
  3. X. Du, L. Sun, W. Duivesteijn, A. Nikolaev, M. Pechenizkiy: Adversarial balancing-based representation learning for causal effect inference with observational data. In: Data Mining and Knowledge Discovery 35, pp. 1713-1738, 2021.
    Impact factor: 5.406.
  4. J.M. Luna, M. Pechenizkiy, W. Duivesteijn, S. Ventura: Exceptional in So Many Ways - Discovering Descriptors that Display Exceptional Behavior on Contrasting Scenarios. In: IEEE Access 8, pp. 200982-200994, 2020.
    Impact factor: 3.367.
  5. W. Duivesteijn, S. Hess, X. Du: How to Cheat the Page Limit. In: WIREs Data Mining and Knowledge Discovery 10 (3), e1361, 2020.
    Impact factor: 7.250.
  6. X. Du, Y. Pei, W. Duivesteijn, M. Pechenizkiy: Exceptional Spatio-Temporal Behavior Mining through Bayesian Non-Parametric Modeling. In: Data Mining and Knowledge Discovery 34, pp. 1267-1290, 2020.
    Impact factor: 3.670.
  7. 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: 2.809.
  8. 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.
  9. 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.
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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. R.M. Schouten, G.W.J.M. Stevens, S.A.F.M. van Dorsselaer, E.L. Duinhof, K. Monshouwer, M. Pechenizkiy, W. Duivesteijn: Analyzing the interplay between societal trends and socio-demographic variables with local pattern mining: Discovering exceptional trends in adolescent alcohol use in the Netherlands. Accepted for presentation at BNAIC/BeNeLearn 2024, to appear.
  2. N.T.J. van den Berg, B.O. Broekgaarden, D.P.A. Mahieu, J.G.M.J. Martens, J.M. Niederle, R.M. Schouten, W. Duivesteijn: Generating MNAR Missingness in Image Data, with Additional Evaluation of MisGAN. Accepted for presentation at BNAIC/BeNeLearn 2024, to appear.
  3. R.M. Schouten, W. Duivesteijn, P.J. Räsänen, J. Paul, M. Pechenizkiy: Exceptional Subitizing Range: Exploring Mathematical Abilities of Finnish Primary School Children with Piecewise Linear Regression. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2024), Part X, pp. 66-82, 2024.
    Acceptance rate: 0.2500 (56 out of 224).
  4. I. Vloothuis, W. Duivesteijn: RMI-RRG: a Soft Protocol to Postulate Monotonicity Constraints for Tabular Datasets. In: Proceedings of the 22nd International Symposium on Intelligent Data Analysis (IDA 2024), Part I, pp. 16-27, 2024.
    Acceptance rate: 0.4040 (40 out of 99).
  5. R.F.A. Verhaegh, J.J.E. Kiezebrink, F. Nusteling, A.W.A. Rio, M.B. Bendiscek, W. Duivesteijn, R.M. Schouten: A Clustering-inspired Quality Measure for Exceptional Preferences Mining - Design Choices and Consequences. In: Proceedings of the 25th International Conference on Discovery Science (DS 2022), pp. 429-444.
    Acceptance rate: 0.4655 (27 out of 58). Including short papers: 0.6724 (39 out of 58).
  6. J.F. van der Haar, S.C. Nagelkerken, I.G. Smit, K. van Straaten, J.A. Tack, R.M. Schouten, W. Duivesteijn: Efficient Subgroup Discovery Through Auto-Encoding. In: Proceedings of the 20th International Symposium on Intelligent Data Analysis (IDA 2022), pp. 327-340.
    Acceptance rate: 0.4366 (31 out of 71).
  7. R.M. Schouten, W. Duivesteijn, M. Pechenizkiy: Exceptional Model Mining for Repeated Cross-Sectional Data (EMM-RCS). In: Proceedings of the 2022 SIAM International Conference on Data Mining (SDM 2022), pp. 585-593.
    Acceptance rate: 0.2785 (83 out of 298).
    A substantially longer version appeared on Figshare.
  8. Y. Soons, R. Dijkman, M. Jilderda, W. Duivesteijn: Predicting Remaining Useful Life with Similarity-based Priors. In: Proceedings of the 18th International Symposium on Intelligent Data Analysis (IDA 2020), pp. 483-495, 2020.
    Acceptance rate: 0.3947 (45 out of 114).
  9. X. Du, Y. Pei, W. Duivesteijn, M. Pechenizkiy: Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data. In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), pp. 3809-3816, 2020.
    Acceptance rate: 0.2056 (1591 out of 7737).
  10. 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), pp. 3-20, 2019.
    Acceptance rate: 0.1771 (130 out of 734).
  11. 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), pp. 257-273, 2019.
    Acceptance rate: 0.1771 (130 out of 734).
  12. 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), pp. 3788-3795, 2019.
    Acceptance rate: 0.1621 (1150 out of 7095).
  13. 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).
  14. 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.
  15. 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).
  16. 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).
  17. 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.
  18. 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).
  19. 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.
  20. 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.
  21. 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).
  22. 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).
  23. 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).
  24. 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.
  25. 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).
  26. 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).
  27. 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).
  28. 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).
  29. 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. W. Duivesteijn, T.C. van Dijk: Exceptional Gestalt Mining: Combining Magic Cards to Make Complex Coalitions Thrive. In: Proceedings of the 8th Workshop on Machine Learning and Data Mining for Sports Analytics, pp. 191-204, 2021.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
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