A. J. Feelders

Michael Mampaey, Siegfried Nijssen, Ad Feelders, Arno J. Knobbe: Efficient Algorithms for Finding Richer Subgroup Descriptions in Numeric and Nominal Data. ICDM 2012: 499-508

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

Diederik M. Roijers, Johan Jeuring, Ad Feelders: Probability estimation and a competence model for rule based e-tutoring systems. LAK 2012: 255-258

Nicola Barile, Ad Feelders: Active Learning with Monotonicity Constraints. SDM 2012: 756-767

Ad Feelders: A new parameter Learning Method for Bayesian Networks with Qualitative Influences. CoRR abs/1206.5245 (2012)

Ad Feelders, Linda C. van der Gaag: Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences. CoRR abs/1207.1387 (2012)

Linda C. van der Gaag, Hans L. Bodlaender, Ad Feelders: Monotonicity in Bayesian Networks. CoRR abs/1207.4160 (2012)

Nicola Barile, Ad Feelders: Monotone Instance Ranking with mira. Discovery Science 2011: 31-45

Barbara F. I. Pieters, Linda C. van der Gaag, Ad Feelders: When Learning Naive Bayesian Classifiers Preserves Monotonicity. ECSQARU 2011: 422-433

Luite Stegeman, Ad Feelders: On Generating All Optimal Monotone Classifications. ICDM 2011: 685-694

Wouter Duivesteijn, Arno J. Knobbe, Ad Feelders, Matthijs van Leeuwen: Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach. ICDM 2010: 158-167

Ad Feelders: Monotone Relabeling in Ordinal Classification. ICDM 2010: 803-808

Rémon van de Kamp, Ad Feelders, Nicola Barile: Isotonic Classification Trees. IDA 2009: 405-416

Linda C. van der Gaag, Silja Renooij, Ad Feelders, Arend de Groote, Marinus J. C. Eijkemans, Frank J. Broekmans, Bart C. J. M. Fauser: Aligning Bayesian Network Classifiers with Medical Contexts. MLDM 2009: 787-801

Jeroen De Knijf, Ad Feelders: An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining. Fundam. Inform. 89(1): 1-22 (2008)

Nicola Barile, Ad Feelders: Nonparametric Monotone Classification with MOCA. ICDM 2008: 731-736

Dennis Leman, Ad Feelders, Arno J. Knobbe: Exceptional Model Mining. ECML/PKDD (2) 2008: 1-16

Wouter Duivesteijn, Ad Feelders: Nearest Neighbour Classification with Monotonicity Constraints. ECML/PKDD (1) 2008: 301-316

A. J. Feelders, Robert van Straalen: Parameter Learning for Bayesian Networks with Strict Qualitative Influences. IDA 2007: 48-58

Ad Feelders: A new parameter Learning Method for Bayesian Networks with Qualitative Influences. UAI 2007: 117-124

A. J. Feelders, Linda C. van der Gaag: Learning Bayesian network parameters under order constraints. Int. J. Approx. Reasoning 42(1-2): 37-53 (2006)

A. J. Feelders, Jevgenijs Ivanovs: Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study. Probabilistic Graphical Models 2006: 75-82

Michael Egmont-Petersen, A. J. Feelders, Bart Baesens: Confidence intervals for probabilistic network classifiers. Computational Statistics & Data Analysis 49(4): 998-1019 (2005)

Arno Siebes, Muhammad Subianto, A. J. Feelders: Instability of Classifiers on Categorical Data. ICDM 2005: 769-772

Eveline M. Helsper, Linda C. van der Gaag, A. J. Feelders, Willie Loeffen, Petra L. Geenen, Armin Elbers: Bringing order into bayesian-network construction. K-CAP 2005: 121-128

A. J. Feelders, Linda C. van der Gaag: Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences. UAI 2005: 193-200

A. Fazel Famili, Joost N. Kok, José María Peña, Arno Siebes, A. J. Feelders (Eds.): Advances in Intelligent Data Analysis VI, 6th International Symposium on Intelligent Data Analysis, IDA 2005, Madrid, Spain, September 8-10, 2005, Proceedings. Lecture Notes in Computer Science 3646, Springer 2005, isbn 3-540-28795-7

Linda C. van der Gaag, Hans L. Bodlaender, A. J. Feelders: Monotonicity in Bayesian Networks. UAI 2004: 569-576

A. J. Feelders, Martijn Pardoel: Pruning for Monotone Classification Trees. IDA 2003: 1-12

Rob Potharst, A. J. Feelders: Classification trees for problems with monotonicity constraints. SIGKDD Explorations 4(1): 1-10 (2002)

A. J. Feelders, H. A. M. Daniels: A general model for automated business diagnosis. European Journal of Operational Research 130(3): 623-637 (2001)

Robert Castelo, A. J. Feelders, Arno Siebes: MAMBO: Discovering Association Rules Based on Conditional Independencies. IDA 2001: 289-298

A. J. Feelders, H. A. M. Daniels, Marcel Holsheimer: Methodological and practical aspects of data mining. Information & Management 37(5): 271-281 (2000)

A. J. Feelders: Credit scoring and reject inference with mixture models. Int. Syst. in Accounting, Finance and Management 9(1): 1-8 (2000)

A. J. Feelders: Prior Knowledge in Economic Applications of Data Mining. PKDD 2000: 395-400

A. J. Feelders: Credit scoring and reject inference with mixture models. Int. Syst. in Accounting, Finance and Management 8(4): 271-279 (1999)

A. J. Feelders: Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation. PKDD 1999: 329-334

A. J. Feelders, Soong Chang, Geoffrey J. McLachlan: Mining in the Presence of Selectivity Bias and its Application to Reject Inference. KDD 1998: 199-203

Jack P. C. Kleijnen, A. J. Feelders, Russell C. H. Cheng: Bootstrapping and Validation of Metamodels in Simulation. Winter Simulation Conference 1998: 701-705

A. J. Feelders: Data mining and related techniques. EUROSIM 1996: 521-527

A. J. Feelders: Learning from Biased Data Using Mixture Models. KDD 1996: 102-107

A. J. Feelders, A. J. F. le Loux, J. W. van't Zand: Data Mining for Loan Evaluation at ABN AMRO: A Case Study. KDD 1995: 106-111