A. J. Feelders

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Explanation and diagnosis in business assessment

Hennie A. M. Daniels, A. J. Feelders, 1992, IEEE Trans. Syst. Man Cybern..

Real-Time Adaptive Residual Calculation for Detecting Trend Deviations in Systems with Natural Variability

Linda C. van der Gaag, A. J. Feelders, Armin Elbers, 2014, IDA.

Data mining and related techniques

A. J. Feelders, A. Feelders, 1996, EUROSIM.

Discussion on the paper by Friedman and Fisher

A. J. Feelders, 1999, Stat. Comput..

On the Statistical Comparison of Inductive Learning Methods

A. J. Feelders, W. J. H. Verkooijen, A. Feelders, 1995, AISTATS.

Learning Bayesian network parameters under order constraints

Linda C. van der Gaag, A. J. Feelders, 2006, Int. J. Approx. Reason..

Pruning for Monotone Classification Trees

A. J. Feelders, Martijn Pardoel, A. Feelders, 2003, IDA.

Learning Bayesian Network Models from Incomplete Data using Importance Sampling

A. J. Feelders, Carsten Riggelsen, 2005, AISTATS.

Credit scoring and reject inference with mixture models

A. J. Feelders, A. Feelders, 2000, Intell. Syst. Account. Finance Manag..

Nonparametric Monotone Classification with MOCA

A. J. Feelders, Nicola Barile, 2008, 2008 Eighth IEEE International Conference on Data Mining.

Exploiting monotonicity constraints to reduce label noise: An experimental evaluation

A. J. Feelders, Tijmen Kolkman, 2016, 2016 International Joint Conference on Neural Networks (IJCNN).

Real-time Adaptive Problem Detection in Poultry

Linda C. van der Gaag, A. J. Feelders, Armin Elbers, 2014, ECAI.

Parameter Learning for Bayesian Networks with Strict Qualitative Influences

A. J. Feelders, Robert van Straalen, 2007, IDA.

Learning from incomplete data in Bayesian networks with qualitative influences

Linda C. van der Gaag, A. J. Feelders, Andrés R. Masegosa, 2016, Int. J. Approx. Reason..

Monotonicity in Bayesian Networks

Linda C. van der Gaag, A. J. Feelders, Hans L. Bodlaender, 2004, UAI.

Real-Time Outlier Detection in Time Series Data of Water Sensors

A. J. Feelders, Luuk van de Wiel, Daniel M. van Es, 2020, AALTD@PKDD/ECML.

Algorithms for Nonparametric Monotone Classification

A. J. Feelders, N. Barile, A. Feelders, 2007 .

Exceptional Model Mining with Tree-Constrained Gradient Ascent

A. J. Feelders, Thomas E. Krak, A. Feelders, 2015, SDM.

Monotone Instance Ranking with mira

A. J. Feelders, Nicola Barile, 2011, Discovery Science.

On Generating All Optimal Monotone Classifications

Luite Stegeman, A. J. Feelders, 2011, 2011 IEEE 11th International Conference on Data Mining.

A model of explanation for financial knowledge-based systems

A. J. Feelders, A. Feelders, 1992 .

Confidence intervals for probabilistic network classifiers

Bart Baesens, Michael Egmont-Petersen, A. J. Feelders, 2005, Comput. Stat. Data Anal..

De petabyte data mining uitdaging

A. J. Feelders, Arno Siebes, Martin Kersten, 2004 .

A Quantitative Comparison of Semantic Web Page Segmentation Approaches

Jurriaan Hage, A. J. Feelders, Robert Kreuzer, 2015, ICWE.

Active Learning with Monotonicity Constraints

A. J. Feelders, Nicola Barile, 2012, SDM.

Nearest Neighbour Classification with Monotonicity Constraints

A. J. Feelders, Wouter Duivesteijn, A. Feelders, 2008, ECML/PKDD.

Credit scoring and reject inference with mixture models

A. J. Feelders, A. Feelders, 2000, Intell. Syst. Account. Finance Manag..

Instability of classifiers on categorical data

A. J. Feelders, Muhammad Subianto, Arno Siebes, 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

Exploiting Monotonicity Constraints in Active Learning for Ordinal Classification

A. J. Feelders, Pieter Soons, 2014, SDM.

Data Mining for Loan Evaluation at ABN AMRO: A Case Study

A. J. Feelders, A. J. F. le Loux, J. W. van't Zand, 1995, KDD.

Learning from Biased Data Using Mixture Models

A. J. Feelders, 1996, KDD.

Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study

A. J. Feelders, Jevgenijs Ivanovs, 2006, Probabilistic Graphical Models.

Probability estimation and a competence model for rule based e-tutoring systems

Johan Jeuring, A. J. Feelders, Diederik M. Roijers, 2012, LAK.

A new parameter Learning Method for Bayesian Networks with Qualitative Influences pdf

A. J. Feelders, 2007, UAI.

Classification trees for problems with monotonicity constraints

A. J. Feelders, Rob Potharst, A. Feelders, 2002, SKDD.

Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences pdf

Linda C. van der Gaag, A. J. Feelders, 2005, UAI.

Efficient algorithms for finding optimal binary features in numeric and nominal labeled data

Siegfried Nijssen, A. J. Feelders, Arno J. Knobbe, 2015, Knowledge and Information Systems.

Learning from biased data using

A. J. Feelders, 1996 .

Isotonic Classification Trees

A. J. Feelders, Nicola Barile, Rémon van de Kamp, 2009, IDA.

MAMBO: Discovering Association Rules Based on Conditional Independencies

A. J. Feelders, Arno Siebes, Robert Castelo, 2001, IDA.

The petabyte data mining challenge

A. J. Feelders, Arno Siebes, Martin Kersten, 2004 .

Monotone Relabeling in Ordinal Classification

A. J. Feelders, 2010, 2010 IEEE International Conference on Data Mining.

When Learning Naive Bayesian Classifiers Preserves Monotonicity

Linda C. van der Gaag, A. J. Feelders, Barbara F. I. Pieters, 2011, ECSQARU.

Aligning Bayesian Network Classifiers with Medical Contexts

Silja Renooij, Linda C. van der Gaag, A. J. Feelders, 2009, MLDM.

Bootstrapping and validation of metamodels in simulation

Jack P. C. Kleijnen, A. J. Feelders, Russell C. H. Cheng, 1998, 1998 Winter Simulation Conference. Proceedings (Cat. No.98CH36274).

Efficient Algorithms for Finding Richer Subgroup Descriptions in Numeric and Nominal Data

Siegfried Nijssen, A. J. Feelders, Arno J. Knobbe, 2012, 2012 IEEE 12th International Conference on Data Mining.

Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation

A. J. Feelders, A. Feelders, 1999, PKDD.

Different slopes for different folks: mining for exceptional regression models with cook's distance

A. J. Feelders, Wouter Duivesteijn, Arno J. Knobbe, 2012, KDD.

Bringing order into bayesian-network construction

Linda C. van der Gaag, A. J. Feelders, Willie Loeffen, 2005, K-CAP '05.

A general model for automated business diagnosis

Hennie A. M. Daniels, A. J. Feelders, H. Daniels, 2001, Eur. J. Oper. Res..

Prior Knowledge in Economic Applications of Data Mining

A. J. Feelders, 2000, PKDD.

Uncovering Document Fraud in Maritime Freight Transport Based on Probabilistic Classification

Hennie A. M. Daniels, A. J. Feelders, Ron Triepels, 2015, International Conference on Computer Information Systems and Industrial Management Applications.

Methodological and practical aspects of data mining

Hennie A. M. Daniels, A. J. Feelders, Marcel Holsheimer, 2000, Inf. Manag..

An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining

A. J. Feelders, Jeroen De Knijf, A. Feelders, 2008, Fundam. Informaticae.

Mining in the Presence of Selectivity Bias and its Application to Reject Inference

Geoffrey J. McLachlan, A. J. Feelders, Soong Chang, 1998, KDD.

BIT Inc. : Project oriented learning in student companies

A. J. Feelders, J. N. Brinkkemper, Antonius A.M. Spil, 1995 .

Data-driven fraud detection in international shipping

A. J. Feelders, Hennie Daniels, Ron Triepels, 2018, Expert Syst. Appl..

Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach

A. J. Feelders, Wouter Duivesteijn, Arno J. Knobbe, 2010, 2010 IEEE International Conference on Data Mining.

Entity Matching in Digital Humanities Knowledge Graphs

A. J. Feelders, Juriaan Baas, Mehdi M. Dastani, 2021, CHR.

Generating Realistic Natural Language Counterfactuals

Floris Bex, A. J. Feelders, Marcel Robeer, 2021, EMNLP.

Exploiting Transitivity for Entity Matching

Mehdi Dastani, A. J. Feelders, Jurian Baas, 2021, ESWC.

Using Machine Learning, Neural Networks and Statistics to Predict Corporate Bankruptcy: A Comparative Study

A. J. Feelders, A. Feelders, P. Pompe, 1996 .

Statistical Concepts for Intelligent Data Analysis

A. J. Feelders, A. Feelders, 2022 .