Barile, N. & Feelders, A.J. (2012). Active Learning with Monotonicity Constraints. In SIAM International Conference on Data Mining (SDM 2012) (pp. 756-767).
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Duivesteijn, W., Feelders, A.J. & Knobbe, A. (2012). Different Slopes for Different Folks: Mining for Exceptional Regression Models with Cook's Distance. In ACM SIGKDD Conference on Knowledge Discovery and Data Mining.
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Mampaey, M.J.R., Nijssen, S., Feelders, A.J. & Knobbe, A. (2012). Efficient Algorithms for Finding Richer Subgroup Descriptions in Numeric and Nominal Data. In Proceedings of the IEEE International Conference on Data Mining (ICDM 2012).
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Roijers, D.M., Jeuring, J.T. & Feelders, A.J. (2012). Probability Estimation and a Competence Model for Rule-based e-Tutoring Systems. In Learning Analytics and Knowledge (LAK 2012).
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Roijers, D.M., Jeuring, J.T. & Feelders, A.J. (2012). Probability estimation and a competence model for rule based e-tutoring systems. Utrecht: Department of Information and Computing Sciences, Utrecht University.
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Barile, N. & Feelders, A.J. (2011). Monotone Instance Ranking with MIRA. In Proceedings of Discovery Science 2011 (pp. 31-45). Berlin: Springer.
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Stegeman, L. & Feelders, A.J. (2011). On generating all optimal monotone classifications. In 11th IEEE International Conference on Data Mining (pp. 685-694).
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Pieters, B.F.I., Gaag, L.C. van der & Feelders, A.J. (2011). When Learning Naive Bayesian Classifiers Preserves Monotonicity. In Proceedings of ECSQARU 2011 (pp. 422-433). Springer.
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Feelders, A.J. (2010). A Decomposition of the Isotonic Regression. : Department of Information and Computing Sciences, Utrecht University.
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Feelders, A.J. (2010). Monotone Relabeling in Ordinal Classification. In B. Liu & G.I. Webb (Eds.), 2010 IEEE International Conference On Data Mining (pp. 803-808). IEEE Computer Society CPS.
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Duivesteijn, W., Knobbe, A.J., Feelders, A.J. & Leeuwen, M. van (2010). Subgroup Discovery meets Bayesian networks – an Exceptional Model Mining approach. In G.I. Webb, B. Liu, C. Zhang, D. Gunopulos & X. Wu (Eds.), Proceedings of the 10th IEEE International Conference on Data Mining (ICDM'10) (pp. 158-167). IEEE.
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Gaag, L.C. van der, Renooij, S., Feelders, A.J., Groote, A.J. de, Eijkemans, M.J.C., Broekmans, F.J. & Fauser, B.C.J.M. (2009). Aligning Bayesian Network Classifiers with Medical Contexts. In P Perner (Ed.), Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition Vol. 5632. Lecture Notes in Computer Science (pp. 787-801). Berlin/Heidelberg: Springer-Verlag.
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Kamp, R. van de, Feelders, A.J. & Barile, N. (2009). Isotonic Classification Trees. In N. Adams (Ed.), Proceedings of IDA 2009 (pp. 405-416). Springer.
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Barile, N. & Feelders, A.J. (2009). Nonparametric Ordinal Classification with Monotonicity Constraints. In A. Feelders & R. Potharst (Eds.), Workshop Proceedings of MoMo 2009 (pp. 47-63).
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Gaag, L.C. van der, Renooij, S., Feelders, A.J., Groote, A.J. de, Eijkemans, M.J.C., Broekmans, F.J. & Fauser, B.C.J.M. (2008). Aligning Bayesian Network Classifiers With Medical Contexts. (UU-CS 2008-015). onbekend: UU WINFI Informatica.
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Knijf, J. de & Feelders, A.J. (2008). An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining. Fundamenta Informaticae, 89(1), 1-22.
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Feelders, A.J. (2008). Credit Scoring. In T. Rudas (Ed.), Handbook of probability: theory and applications (pp. 343-362). Sage.
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Kamphuis, C., Mollenhorst, H., Feelders, A.J. & Hogeveen, H. (2008). Decision tree induction for detection of clinical mastitis using data from six Dutch dairy herds milking with an automatic milking system. In T.J.G.M. Lam (Ed.), Mastitis control: From science to practice (pp. 267-274).
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Leman, D., Feelders, A.J. & Knobbe, A.J. (2008). Exceptional Model Mining. In Proceedings of ECML PKDD, Part II (pp. 1-16).
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Duivesteijn, W. & Feelders, A.J. (2008). Nearest Neighbour Classification with Monotonicity Constraints. In W. Daelemans (Ed.), Proceedings of ECML/PKDD 2008 (pp. 301-316). Springer.
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Barile, N. & Feelders, A.J. (2008). Nonparametric Monotone Classification with MOCA. In F. Giannotti (Ed.), Proceedings of the Eighth IEEE International Conference on Data Mining (ICDM 2008) (pp. 731-736). IEEE Computer Society.
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Feelders, A.J. (2007). A new parameter learning method for Bayesian networks with qualitative influences. In R. Parr & L.C. van der Gaag (Eds.), Proceedings of Uncertainty in Artificial Intelligence 2007 (UAI07) (pp. 117-124). AUAI Press.
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Knijf, J. de & Feelders, A.J. (2007). Choosing the Right Patterns: An Experimental Comparison between Different Tree Inclusion Relations. In D. Malerba, A. Appice & M. Ceci (Eds.), Proceedings of the Sixth International Workshop on Multi-Relational Data Mining (pp. 10-21).
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Feelders, A.J. & Straalen, R. van (2007). Parameter Learning for Bayesian Networks with Strict Qualitative Influences. In M.R. Berthold, J. Shawe-Taylor & N. Lavrac (Eds.), Advances in Intelligent Data Analysis VII (pp. 48-58). Springer.
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Feelders, A.J. & Ivanovs, J. (2006). Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study. In M. Studen'y & J. Vomlel (Eds.), Proceedings of the third European workshop on probabilistic graphical models (PGM'06) (pp. 75-82).
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Feelders, A.J. & Gaag, L.C. van der (2006). Learning Bayesian network parameters under order constraints. International Journal of Approximate Reasoning, 42, 37-53.
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Velikova, M., Daniels, H & Feelders, A.J. (2006). Mixtures of Monotone Networks for Prediction. International Journal of Computational Intelligence, 3, 204-214.
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Velikova, M., Daniels, H & Feelders, A.J. (2006). Solving partially monotone problems with neural networks. In R. Damasevicius (Ed.), Transactions on Engineering, Computing, and Technology (pp. 82-87).
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Feelders, A.J., Velikova, M. & Daniels, H (2006). Two polynomial algorithms for relabeling non-monotone data. onbekend: UU WINFI Informatica en Informatiekunde.
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Famili, A.F., Kok, J.N., Peña, A.S., Siebes, A.P.J.M. & Feelders, A.J. (Eds.). (2005). Advances in Intelligent Data Analysis VI, 6th International Symposium on Intelligent Data Analysis, IDA 2005 (Lecture Notes in Computer Science, 3646). Berlin: Springer-Verlag.
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Helsper, E.M., Gaag, L.C. van der, Feelders, A.J., Loeffen, W.L.A., Geenen, P.L. & Elbers, A.R.W. (2005). Bringing order into Bayesian-network construction. In Proceedings of the Third International Conference on Knowledge Capture (pp. 121-128). New York: ACM Press.
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Egmont-Petersen, M., Feelders, A.J. & Baesens, B. (2005). Confidence intervals for probabilistic network classifiers. Computational Statistics and Data Analysis, 49(4), 998-1019.
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Siebes, A.P.J.M., Subianto, M. & Feelders, A.J. (2005). Instability of Classifiers on Categorical Data. In J. Han, B.W. Wah, V. Raghavan, X. Wu & R. Rastogi (Eds.), Proceedings of the 5th IEEE International Conference on Data Mining (ICDM 2005) (pp. 769-772). IEEE Computer Society.
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Feelders, A.J. & Gaag, L.C. van der (2005). Learning Bayesian network parameters with prior knowledge about context-specific qualitative influences. In F. Bacchus & T. Jaakkola (Eds.), Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence, (pp. 193-200). Corvallis: AUAI Press.
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Riggelsen, C. & Feelders, A.J. (2005). Learning Bayesian Network Models from Incomplete Data using Importance Sampling. In Z. Ghahramani & R. Cowell (Eds.), Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (pp. 301-308). Society for Artificial Intellligence and Statistics.
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Feelders, A.J. & Gaag, L.C. van der (2005). Learning Bayesian Network Parameters Under Order Constraints. (UU-CS 2005-58). onbekend: UU WINFI Informatica en Informatiekunde.
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Knijf, J. de & Feelders, A.J. (2005). Monotone Constraints in Frequent Tree mining. In M. van Otterlo, M. Poel & A. Nijholt (Eds.), BENELEARN:Proceedingd of the 14 th Annual Machine Learning Conference of Belgium and the Netherlands (pp. 13-20).
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Feelders, A.J. & Gaag, L.C. van der (2004). Learning Bayesian Network Parameters Under Order Constraints. In P. Lucas (Ed.), Proceedings of the second European workshop on probabilistic graphical models (PGM'04) (pp. 73-80).
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Gaag, L.C. van der, Bodlaender, H.L. & Feelders, A.J. (2004). Monotonicity in Bayesian Networks. In M. Chickering & J. Halpern (Eds.), Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (pp. 569-576). AUAI Press.
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Feelders, A.J. (2003, October 11). An overview of model-based reject inference for credit scoring. Banff, Canada, Banff Credit Risk Conference 2003.
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Feelders, A.J. (Ed.). (2003). Knowledge Discovery in Databases: PKDD 2003 (LNAI, 2838). Berlijn: Springer.
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Feelders, A.J. & Pardoel, M. (2003). Pruning for Monotone Classification Trees. In M.R. Berthold, H.J. Lenz, E. Bradley, R. Kruse & C. Borgelt (Eds.), Advances in Intelligent Data Analysis V. Berlijn: Springer.
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Feelders, A.J. (2003). Reject inference: distinguishing ignorable and non-ignorable selection mechanisms. Credit Risk International, 10-14.
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Feelders, A.J. (2003). Statistical Concepts. In M. Berthold & D.J. Hand (Eds.), Intelligent Data Analysis: an introduction (2nd edition). Berlijn: Springer.
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Feelders, A.J. (Ed.). (2003). International journal of intelligent systems in accounting, finance & management, 12.
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Feelders, A.J. (2002, December 11). Advances in Data Mining. Eindhoven, the Netherlands, SOIA 2002 (1st Philips symposium on intelligent algorithms).
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Potharst, R. & Feelders, A.J. (2002). Classification Trees for Problems with Monotonicity Constraints. SIGKDD Explorations, 4(1), 1-10.
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Feelders, A.J. (2002). Clustering. In J. Meij (Ed.), Dealing with the data flood (STT, 65) (pp. 629-634). Den Haag, the Netherlands: STT/Beweton.
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Feelders, A.J. (2002). Data Mining in Economic Science. In J. Meij (Ed.), Dealing with the data flood (STT, 65) (pp. 166-175). Den Haag, the Netherlands: STT/Beweton.
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Feelders, A.J. (2002, January 17). Handling missing data in data mining. Berlijn, Germany, Colloquium kwantitatieve economie: Vrije Universiteit Berlijn.
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Feelders, A.J. (2002). Rule induction by bump hunting. In J. Meij (Ed.), Dealing with the data flood (STT, 65) (pp. 697-700). Den Haag, the Netherlands: STT/Beweton.
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Feelders, A.J. (Ed.). (2002). International journal of intelligent systems in accounting, finance & management.
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Feelders, A.J. & Daniels, H.A.M. (2001). A general model for automated business diagnosis. European Journal of Operational Research, 130(3), 623-637.
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Castelo Valdueza, R., Feelders, A.J. & Siebes, A.P.J.M. (2001). Mambo: Discovering Association Rules Based on Conditional Independencies. In F. Hoffmann, D.J. Hand, N. Adams, D. Fisher & G. Guimaraes (Eds.), Advances in Intelligent Data Analysis (pp. 289-298). Berlin, Heidelberg, New York: Springer Verlag.
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Siebes, A.P.J.M. & Feelders, A.J. (2001). OFFER: Making the right offer at customer contact. In V. Hoste & G. de Pauw (Eds.), Proceedings of the eleventh Belgian-Dutch Conference on Machine Learning (pp. 55-60). Antwerpen, Belgium.
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Feelders, A.J. (2001). Referee-schap. Conferentie: ECML 2001.
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Feelders, A.J. (2001). Referee-schap. Conferentie: PKDD 2001.
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Feelders, A.J. (Ed.). (2001). International journal of intelligent systems in accounting, finance & management, 10.
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