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ORCIDA. J. Feelders
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- affiliation: Utrecht University, Netherlands
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Journal Articles
- 2018
[j14]Ron Triepels, Hennie Daniels, Ad Feelders:
Data-driven fraud detection in international shipping. Expert Syst. Appl. 99: 193-202 (2018)- 2016
- [j13]Wouter Duivesteijn, Ad Feelders, Arno J. Knobbe
:
Exceptional Model Mining - Supervised descriptive local pattern mining with complex target concepts. Data Min. Knowl. Discov. 30(1): 47-98 (2016) - [j12]Andrés R. Masegosa, A. J. Feelders, Linda C. van der Gaag:
Learning from incomplete data in Bayesian networks with qualitative influences. Int. J. Approx. Reason. 69: 18-34 (2016) - 2015
- [j11]Michael Mampaey, Siegfried Nijssen, Ad Feelders, Rob M. Konijn, Arno J. Knobbe:
Efficient algorithms for finding optimal binary features in numeric and nominal labeled data. Knowl. Inf. Syst. 42(2): 465-492 (2015) - 2008
- [j10]Jeroen De Knijf, Ad Feelders:
An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining. Fundam. Informaticae 89(1): 1-22 (2008) - 2006
- [j9]A. J. Feelders, Linda C. van der Gaag:
Learning Bayesian network parameters under order constraints. Int. J. Approx. Reason. 42(1-2): 37-53 (2006) - 2005
- [j8]Michael Egmont-Petersen, A. J. Feelders, Bart Baesens:
Confidence intervals for probabilistic network classifiers. Comput. Stat. Data Anal. 49(4): 998-1019 (2005) - 2002
- [j7]Rob Potharst, A. J. Feelders:
Classification trees for problems with monotonicity constraints. SIGKDD Explor. 4(1): 1-10 (2002) - 2001
- [j6]A. J. Feelders, Hennie A. M. Daniels:
A general model for automated business diagnosis. Eur. J. Oper. Res. 130(3): 623-637 (2001) - 2000
- [j5]A. J. Feelders, Hennie A. M. Daniels, Marcel Holsheimer:
Methodological and practical aspects of data mining. Inf. Manag. 37(5): 271-281 (2000) - [j4]A. J. Feelders:
Credit scoring and reject inference with mixture models. Intell. Syst. Account. Finance Manag. 9(1): 1-8 (2000) - 1999
- [j3]A. J. Feelders:
Credit scoring and reject inference with mixture models. Intell. Syst. Account. Finance Manag. 8(4): 271-279 (1999) - [j2]A. J. Feelders:
Discussion on the paper by Friedman and Fisher. Stat. Comput. 9(2): 147-148 (1999) - 1992
- [j1]Hennie A. M. Daniels, Ad Feelders:
Explanation and diagnosis in business assessment. IEEE Trans. Syst. Man Cybern. 22(2): 397-402 (1992)
Conference and Workshop Papers
- 2023
- [c48]Marcel Robeer, Floris Bex, Ad Feelders, Henry Prakken:
Explaining Model Behavior with Global Causal Analysis. xAI (1) 2023: 299-323 - 2022
- [c47]Jurian Baas, Leon van Wissen, Jirsi Reinders, Mehdi M. Dastani, A. J. Feelders:
Adding Domain Knowledge to Improve Entity Resolution in 17th and 18th Century Amsterdam Archival Records. SEMANTiCS 2022: 90-104 - 2021
- [c46]Laurens P. Stoop, Erik Duijm, Ad Feelders, Machteld van den Broek:
Detection of Critical Events in Renewable Energy Production Time Series. AALTD@ECML/PKDD 2021: 104-119 - [c45]Juriaan Baas, Mehdi M. Dastani, Ad Feelders:
Entity Matching in Digital Humanities Knowledge Graphs. CHR 2021: 1-15 - [c44]Marcel Robeer, Floris Bex, Ad Feelders:
Generating Realistic Natural Language Counterfactuals. EMNLP (Findings) 2021: 3611-3625 - [c43]Jurian Baas, Mehdi Dastani, A. J. Feelders:
Exploiting Transitivity for Entity Matching. ESWC (Satellite Events) 2021: 109-114 - 2020
- [c42]Jurian Baas, Mehdi Dastani, Ad Feelders:
Tailored Graph Embeddings for Entity Alignment on Historical Data. iiWAS 2020: 125-133 - [c41]Luuk van de Wiel, Daniel M. van Es, A. J. Feelders:
Real-Time Outlier Detection in Time Series Data of Water Sensors. AALTD@PKDD/ECML 2020: 155-170 - 2019
- [c40]Bart J. van der Lugt, Ad Feelders:
Conditional Forecasting of Water Level Time Series with RNNs. AALTD@PKDD/ECML 2019: 55-71 - 2016
- [c39]Ad Feelders, Tijmen Kolkman:
Exploiting monotonicity constraints to reduce label noise: An experimental evaluation. IJCNN 2016: 2148-2155 - 2015
- [c38]Ron Triepels, Ad Feelders, Hennie A. M. Daniels:
Uncovering Document Fraud in Maritime Freight Transport Based on Probabilistic Classification. CISIM 2015: 282-293 - [c37]Robert Kreuzer, Jurriaan Hage, Ad Feelders:
A Quantitative Comparison of Semantic Web Page Segmentation Approaches. ICWE 2015: 374-391 - [c36]Thomas E. Krak, Ad Feelders:
Exceptional Model Mining with Tree-Constrained Gradient Ascent. SDM 2015: 487-495 - 2014
- [c35]Steven P. D. Woudenberg, Linda C. van der Gaag, Ad Feelders, Armin R. W. Elbers:
Real-time Adaptive Problem Detection in Poultry. ECAI 2014: 1217-1218 - [c34]Steven P. D. Woudenberg, Linda C. van der Gaag, Ad Feelders, Armin R. W. Elbers:
Real-Time Adaptive Residual Calculation for Detecting Trend Deviations in Systems with Natural Variability. IDA 2014: 380-392 - [c33]Pieter Soons, Ad Feelders:
Exploiting Monotonicity Constraints in Active Learning for Ordinal Classification. SDM 2014: 659-667 - 2012
- [c32]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 - [c31]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 - [c30]Diederik M. Roijers, Johan Jeuring, Ad Feelders:
Probability estimation and a competence model for rule based e-tutoring systems. LAK 2012: 255-258 - [c29]Nicola Barile, Ad Feelders:
Active Learning with Monotonicity Constraints. SDM 2012: 756-767 - 2011
- [c28]Nicola Barile, Ad Feelders:
Monotone Instance Ranking with mira. Discovery Science 2011: 31-45 - [c27]Barbara F. I. Pieters, Linda C. van der Gaag, Ad Feelders:
When Learning Naive Bayesian Classifiers Preserves Monotonicity. ECSQARU 2011: 422-433 - [c26]Luite Stegeman, Ad Feelders:
On Generating All Optimal Monotone Classifications. ICDM 2011: 685-694 - 2010
- [c25]Wouter Duivesteijn, Arno J. Knobbe, Ad Feelders, Matthijs van Leeuwen:
Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach. ICDM 2010: 158-167 - [c24]Ad Feelders:
Monotone Relabeling in Ordinal Classification. ICDM 2010: 803-808 - 2009
- [c23]Rémon van de Kamp, Ad Feelders, Nicola Barile:
Isotonic Classification Trees. IDA 2009: 405-416 - [c22]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 - 2008
- [c21]Nicola Barile, Ad Feelders:
Nonparametric Monotone Classification with MOCA. ICDM 2008: 731-736 - [c20]Dennis Leman, Ad Feelders, Arno J. Knobbe:
Exceptional Model Mining. ECML/PKDD (2) 2008: 1-16 - [c19]Wouter Duivesteijn, Ad Feelders:
Nearest Neighbour Classification with Monotonicity Constraints. ECML/PKDD (1) 2008: 301-316 - 2007
- [c18]A. J. Feelders, Robert van Straalen:
Parameter Learning for Bayesian Networks with Strict Qualitative Influences. IDA 2007: 48-58 - [c17]Ad Feelders:
A new parameter Learning Method for Bayesian Networks with Qualitative Influences. UAI 2007: 117-124 - 2006
- [c16]A. J. Feelders, Jevgenijs Ivanovs:
Discriminative Scoring of Bayesian Network Classifiers: a Comparative Study. Probabilistic Graphical Models 2006: 75-82 - 2005
- [c15]Carsten Riggelsen, Ad Feelders:
Learning Bayesian Network Models from Incomplete Data using Importance Sampling. AISTATS 2005: 301-308 - [c14]Arno Siebes, Muhammad Subianto, A. J. Feelders:
Instability of Classifiers on Categorical Data. ICDM 2005: 769-772 - [c13]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 - [c12]A. J. Feelders, Linda C. van der Gaag:
Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences. UAI 2005: 193-200 - 2004
- [c11]Linda C. van der Gaag, Hans L. Bodlaender, A. J. Feelders:
Monotonicity in Bayesian Networks. UAI 2004: 569-576 - 2003
- [c10]A. J. Feelders, Martijn Pardoel:
Pruning for Monotone Classification Trees. IDA 2003: 1-12 - 2001
- [c9]Robert Castelo, A. J. Feelders, Arno Siebes:
MAMBO: Discovering Association Rules Based on Conditional Independencies. IDA 2001: 289-298 - 2000
- [c8]A. J. Feelders:
Prior Knowledge in Economic Applications of Data Mining. PKDD 2000: 395-400 - 1999
- [c7]A. J. Feelders:
Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation. PKDD 1999: 329-334 - 1998
- [c6]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 - [c5]Jack P. C. Kleijnen, A. J. Feelders, Russell C. H. Cheng:
Bootstrapping and Validation of Metamodels in Simulation. WSC 1998: 701-705 - 1996
- [c4]A. J. Feelders:
Data mining and related techniques. EUROSIM 1996: 521-527 - [c3]A. J. Feelders:
Learning from Biased Data Using Mixture Models. KDD 1996: 102-107 - 1995
- [c2]A. J. Feelders, W. J. H. Verkooijen:
On the Statistical Comparison of Inductive Learning Methods. AISTATS 1995: 271-279 - [c1]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
Editorship
- 2020
- [e3]Michael R. Berthold, Ad Feelders, Georg Krempl:
Advances in Intelligent Data Analysis XVIII - 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27-29, 2020, Proceedings. Lecture Notes in Computer Science 12080, Springer 2020, ISBN 978-3-030-44583-6 [contents] - 2014
- [e2]Linda C. van der Gaag, A. J. Feelders:
Probabilistic Graphical Models - 7th European Workshop, PGM 2014, Utrecht, The Netherlands, September 17-19, 2014. Proceedings. Lecture Notes in Computer Science 8754, Springer 2014, ISBN 978-3-319-11432-3 [contents] - 2005
- [e1]A. Fazel Famili, Joost N. Kok, José María Peña Sánchez, Arno Siebes, A. J. Feelders:
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 [contents]
Informal and Other Publications
- 2024
- [i6]Laurens P. Stoop, Erik Duijm, A. J. Feelders, Machteld van den Broek:
Detection of Critical Events in Renewable Energy Production Time Series. CoRR abs/2401.17814 (2024) - [i5]Michiel P. Bron, Peter G. M. van der Heijden, A. J. Feelders, Arno P. J. M. Siebes:
Using Chao's Estimator as a Stopping Criterion for Technology-Assisted Review. CoRR abs/2404.01176 (2024) - 2021
- [i4]Jurian Baas, Mehdi Dastani, Ad Feelders:
Exploiting Transitivity Constraints for Entity Matching in Knowledge Graphs. CoRR abs/2104.12589 (2021) - 2012
- [i3]Ad Feelders:
A new parameter Learning Method for Bayesian Networks with Qualitative Influences. CoRR abs/1206.5245 (2012) - [i2]Ad Feelders, Linda C. van der Gaag:
Learning Bayesian Network Parameters with Prior Knowledge about Context-Specific Qualitative Influences. CoRR abs/1207.1387 (2012) - [i1]Linda C. van der Gaag, Hans L. Bodlaender, Ad Feelders:
Monotonicity in Bayesian Networks. CoRR abs/1207.4160 (2012)
Coauthor Index
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