Ad Feelders

Ad Feelders
CGN Room A105, tel +31-30-2533176
Institute of Information & Computing Sciences
P.O.Box 80.089
3508 TB Utrecht

Short Bio

Dr. A.J. Feelders (1964) is assistant professor at Utrecht University, Institute of Information and Computing Sciences, since january 2001. He has worked as a data mining consultant for Data Distilleries, where he was in charge of data mining projects for banking, insurance as well as retail companies. Before coming to Utrecht he was an assistant professor at the department of Economics of Tilburg University in The Netherlands. He has published several articles on data mining in international data mining conference proceedings (KDD and PKDD) and international journals. He is a member of the editorial board of International Journal of Intelligent Systems in Accounting, Finance and Management (Wiley).

Some recent publications

• Nicola Barile and Ad Feelders, Nonparametric Monotone Classification with MOCA, in F. Giannotti et al.:
  Proceedings Eighth IEEE International Conference on Data Mining (ICDM-2008), pp. 731-736, 2008.
• Wouter Duivesteijn and Ad Feelders, Nearest Neighbour Classification with Monotonicity Constraints, in W. Daelemans et al. (eds):
  Proceedings of ECML/PKDD 2008, Part I, LNAI 5211, pp. 301-316, Springer, 2008.
• Dennis Leman, Ad Feelders, and Arno Knobbe: Exceptional Model Mining, in W. Daelemans et al. (eds):
  Proceedings of ECML/PKDD 2008, Part II, LNAI 5212, pp. 1-16, Springer, 2008.
• Ad Feelders, Credit Scoring, in T. Rudas (ed.), Handbook of Probability: Theory and Applications, Sage, 2008.
• Ad Feelders and Robert van Straalen (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, Springer, pp. 48-58.
• Ad Feelders (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, AUAI Press, Corvallis (Oregon), pp. 117--124.
• A. Feelders and J. Ivanovs (2006), Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study,
  In: M. Studeny and J. Vomlel (eds.) Proceedings of the third European workshop on Probabilistic Graphical Models (PGM'06), pp. 75-82.
• A. Feelders and L.C. van der Gaag (2006), Learning Bayesian network parameters under order constraints, International Journal of Approximate Reasoning, Volume 42, Issues 1-2 , May 2006, Pages 37-53.
• M. Velikova, H. Daniels, A. Feelders (2006). Mixtures of Monotone Networks for Prediction, International Journal of Computational Intelligence 3 (3), pp. 204-214.
• M. Velikova, H. Daniels, A. Feelders (2006). Solving partially monotone problems with neural networks Proceedings of ICCS'06 Vienna, Austria,
  Transactions on Engineering, Computing, and Technology, Volume 12, pp. 82-87.
• E.M. Helsper, L.C. van der Gaag, A.J. Feelders, W.L.A. Loeffen, P.L. Geenen, A.R.W. Elbers (2005). Bringing order into Bayesian-network construction
  Proceedings of the Third International Conference on Knowledge Capture, ACM Press, New York, pp. 121 - 128.
• Ad Feelders and Linda C. van der Gaag (2005), Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences,
  in: F. Bacchus, T. Jaakkola (eds.) Proceedings of Uncertainty in Artificial Intelligence 2005, AUAI Press, Corvallis (Oregon), pp. 193--200.
• M. Egmont-Petersen, A. Feelders, and B. Baesens (2005), Confidence intervals for probabilistic network classifiers, Computational Statistics & Data Analysis, Volume 49, Issue 4 , pp. 998--1019.
• Carsten Riggelsen and Ad Feelders (2005), Learning Bayesian Network Models from Incomplete Data using Importance Sampling, in Robert G. Cowell and Zoubin Ghahramani (eds):
  Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, Society for Artificial Intelligence and Statistics, pp 301--308.
• Ad Feelders and Linda C. van der Gaag (2004) Learning Bayesian Network Parameters Under Order Constraints,
  Second European Workshop on Probabilistic Graphical Models 2004 (PGM04),4-8 October 2004, Leiden, The Netherlands.
• Linda C. van der Gaag, Hans L. Bodlaender and Ad Feelders (2004) Monotonicity in Bayesian Networks, in: M. Chickering, J. Halpern (eds.) Proceedings of Uncertainty in Artificial Intelligence 2004,
  AUAI Press, Arlington (Virginia), pp. 569--576.
• Ad Feelders (2003) Reject inference: distinguishing ignorable and non-ignorable selection mechanisms, Credit Risk International, December 2003 - January 2004, pp. 10-14.
• A.J. Feelders and M. Pardoel (2003) Pruning for Monotone Classification Trees, in: M.R. Berthold et al. (eds.) Advanced in intelligent data analysis V, Springer LNCS 2810, Berlin, pp. 1-12.
• A.J. Feelders (2003) Statistical Concepts, in: Intelligent Data Analysis: an introduction (2nd revised and extended edition), M. Berthold and D.J. Hand (eds.), Springer, Berlin, pp.17-68.
• R. Potharst and A. Feelders (2002), Classification Trees for Problems with Monotonicity Constraints, SIGKDD Explorations 4(1), pp. 1-10.
• Robert Castelo, Ad Feelders and Arno Siebes (2001), MAMBO: Discovering Association Rules Based on Conditional Independencies, In F. Hoffmann et al. (eds) Advances in Intelligent Data Analysis, Springer LNCS 2189,pp. 289-298.
• A.J. Feelders and H.A.M. Daniels (2001), A general model for automated business diagnosis , European Journal of Operational Research 130, pp. 623-637.
• A.J. Feelders, H.A.M. Daniels and M. Holsheimer (2000), Methodological and practical aspects of data mining, Information & Management 37(5), pp. 271-281.
• A.J. Feelders (2000) Credit scoring and reject inference with mixture models ,International Journal of Intelligent Systems in Accounting, Finance and Management 9, pp. 1-8.
• A.J. Feelders (2000) Prior knowledge in economic applications of data mining , Proceedings of the fourth European conference on principles and practice of knowledge discovery in data bases, Springer, pp. 395-400.
• A.J. Feelders (1999) Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation?, Proceedings of the third European conference on principles and practice of knowledge discovery in data bases (PKDD99), Springer, pp. 329-334. A slightly longer version of this paper can be downloaded here.
• A.J. Feelders (1999) Statistical Concepts, in: Intelligent Data Analysis: an introduction, M. Berthold and D.J. Hand (eds.), Springer, Berlin, pp.15-66.
• A.J. Feelders (1999), Discussion of "Bump Hunting in High-Dimensional Data" by J.H. Friedman and N.I. Fisher, Statistics and Computing 9 (2), pp.147-148.

Unpublished manuscripts, presentations, etc.

• A. Feelders and J. Ivanovs, Discriminative Structure Learning of Bayesian Network Classifiers through Logistic Regression, unpublished manuscript, 2006.
• A.J. Feelders, Data Mining in Economic Science, unpublished.
• A.J. Feelders, An Overview of Model Based Reject Inference for Credit Scoring, unpublished.
• A.J. Feelders, An Overview of Model Based Reject Inference for Credit Scoring, presentation at Banff retail credit risk conference 2003.

Research Interests

• Algorithms for Monotone Classification
• Handling Incomplete Data in Data Mining (PhD Student: Carsten Riggelsen)
• Use of Prior Knowledge in Learning from Data
• Reject Inference for Credit Risk Modelling
• Frequent Pattern Mining (PhD Student: Jeroen de Knijf)

MSc Projects (first supervisor)

• Data Mining with classification trees for problems with monotonicity constraints.
  Student: M. Pardoel (finished May 2003)
• Data Mining with Monotonicity Constraints

Master Program Applied Computing Science