Publications
2023
Scholarly publications
Stoop, L. P., Wiel, K. V. D., Zappa, W., Haverkamp, A., Feelders, A. J., & Broek, M. V. D. (2023). The Climatological Renewable Energy Deviation Index. (pp. 1-36). arXiv. https://doi.org/10.48550/arXiv.2307.08909
Robeer, M., Bex, F., Feelders, A., & Prakken, H. (Accepted/In press). Explaining Model Behavior with Global Causal Analysis. In Proceedings of the 1st World Conference on eXplainable Artificial Intelligence (xAI 2023) Springer LNCS.
2022
Scholarly publications
Baas, J., van Wissen, L., Reinders, J., Dastani, M., & Feelders, A. (2022). Adding Domain Knowledge to Improve Entity Resolution in 17th and 18th Century Amsterdam Archival Records. 90-104. Paper presented at SEMANTiCS 2022, Vienna, Austria. https://doi.org/10.3233/SSW220012
https://dspace.library.uu.nl/bitstream/handle/1874/423192/Occasional_Poetry_SEMANTiCS_2022_pure.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/423192/Occasional_Poetry_SEMANTiCS_2022_pure.pdf?sequence=1
2021
Scholarly publications
Baas, J., Dastani, M., & Feelders, A. (2021). Exploiting Transitivity for Entity Matching. In R. Verborgh, A. Dimou, A. Hogan, C. d'Amato, I. Tiddi, A. Bröring, S. Mayer, F. Ongenae, R. Tommasini, & M. Alam (Eds.), The Semantic Web: ESWC 2021 Satellite Events: Virtual Event, June 6–10, 2021, Revised Selected Papers (pp. 109-114). (Lecture Notes in Computer Science; Vol. 12739). Springer. https://doi.org/10.1007/978-3-030-80418-3_20
Baas, J., Dastani, M., & Feelders, A. (2021). Entity Matching in Digital Humanities Knowledge Graphs. 1-15. Paper presented at Computational Humanities Research, Amsterdam, Netherlands.
https://dspace.library.uu.nl/bitstream/handle/1874/423193/long_paper5.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/423193/long_paper5.pdf?sequence=1
Robeer, M., Bex, F., & Feelders, A. (2021). Generating Realistic Natural Language Counterfactuals. In M-F. Moens, X. Huang, L. Specia, & S. W. Yih (Eds.), Findings of the Association for Computational Linguistics: EMNLP 2021 (pp. 3611–3625). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2021.findings-emnlp.306
Stoop, L. P., Duijm, E., Feelders, A., & Broek, M. V. D. (2021). Detection of Critical Events in Renewable Energy Production Time Series. In V. Lemaire, S. Malinowski, A. Bagnall, T. Guyet, R. Tavenard, & G. Ifrim (Eds.), Advanced Analytics and Learning on Temporal Data: 6th ECML PKDD Workshop, AALTD 2021, Bilbao, Spain, September 13, 2021, Revised Selected Papers (1 ed., pp. 104-119). (Lecture Notes in Computer Science; Vol. 13114). Springer Cham. https://doi.org/10.1007/978-3-030-91445-5_7
2020
Scholarly publications
van der Lugt, B., & Feelders, A. J. (2020). Conditional Forecasting of Water Level Time Series with RNNs. In V. Lemaire, S. Malinowski, A. Bagnall, A. Bondu, T. Guyet, & R. Tavenard (Eds.), Advanced Analytics and Learning on Temporal Data: 4th ECML PKDD Workshop, AALTD 2019, Würzburg, Germany, September 20, 2019, Revised Selected Papers (pp. 55-71). (Lecture Notes in Computer Science; Vol. 11986). Springer Cham. https://doi.org/10.1007/978-3-030-39098-3_5
Baas, J., Dastani, M. M., & Feelders, A. J. (2020). Tailored Graph Embeddings for Entity Alignment on Historical Data. 125--133. Paper presented at International Conference on Information Integration and Web-based Applications & Services. https://doi.org/10.1145/3428757.3429111
van de Wiel, L., van Es, D., & Feelders, A. J. (2020). Real-Time Outlier Detection in Time Series Data of Water Sensors. In V. Lemaire, S. Malinowski, A. Bagnall, T. Guyet, R. Tavenard, & G. Ifrim (Eds.), Advanced Analytics and Learning on Temporal Data. : AALTD 2020. (Vol. 12588, pp. 155-170). Springer Cham. https://doi.org/10.1007/978-3-030-65742-0_11
Berthold, M., Feelders, A. J., & Krempl, G. M. (Eds.) (2020). Advances in Intelligent Data Analysis XVIII. (LNCS), (Information Systems and Applications, incl. Internet/Web, and HCI ; Vol. 12080). SPRING. https://doi.org/10.1007/978-3-030-44584-3
Omta, W., van Heesbeen, R., Shen, Z., de Nobel, J., van der Velden, L., Medema, R., Siebes, A., Feelders, A., Brinkkemper, S., Klumperman, J., Spruit, M., Brinkhuis, M., & Egan, D. (2020). Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening. SLAS Discovery, 25(6), 655–664. https://doi.org/10.1177/2472555220919345
Omta, W., Heesbeen, R. V., Shen, I., Feelders, A., Brinkhuis, M., Egan, D., & Spruit, M. (2020). PurifyR: an R Package for highly automated reproducible variable extraction and standardization. Families, Systems and Health, 3(1), 1-7. https://doi.org/10.1089/sysm.2019.0007
2019
Scholarly publications
Baas, J., Feelders, A. J., & Dastani, M. M. (2019). Graph Embeddings for Enrichment of Historical Data. Poster session presented at Workshop on Graph Embedding and Data Mining(GEM) 2019, Würzburg, Bavaria, Germany.
https://dspace.library.uu.nl/bitstream/handle/1874/415596/ECML_PKDD_GEM_2019.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/415596/ECML_PKDD_GEM_2019.pdf?sequence=1
2018
Scholarly publications
Triepels, R., Daniels, H., & Feelders, A. J. (2018). Data-driven fraud detection in international shipping. Expert Systems with Applications, 99, 193-202. https://doi.org/10.1016/j.eswa.2018.01.007
2016
Scholarly publications
Masegosa, A., Feelders, A. J., & van der Gaag, L. C. (2016). Learning from incomplete data in Bayesian networks with qualitative influences. International Journal of Approximate Reasoning, 69(C), 18-34 . https://doi.org/10.1016/j.ijar.2015.11.004
Feelders, A. J., & Kolkman, T. (2016). Exploiting Monotonicity Constraints to Reduce Label Noise: an Experimental Evaluation. In 2016 International Joint Conference on Neural Networks (pp. 2148-2155). IEEE. https://doi.org/10.1109/IJCNN.2016.7727465
2015
Scholarly publications
Krak, T., & Feelders, A. J. (2015). Exceptional Model Mining with Tree-Constrained Gradient Ascent. (Technical Report Series; No. UU-CS-2015-002). UU BETA ICS Departement Informatica.
https://dspace.library.uu.nl/bitstream/handle/1874/327007/2015_002.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/327007/2015_002.pdf?sequence=1
Triepels, R., Feelders, A. J., & Daniels, H. (2015). Uncovering Document Fraud in Maritime Freight Transport Based on Probabilistic Classification. In CISIM 2015 (pp. 282-293). (Lecture Notes in Computer Science; Vol. 9339). Springer. https://doi.org/10.1007/978-3-319-24369-6_23
Kreuzer, R., Hage, J., & Feelders, A. J. (2015). A Quantitative Comparison of Semantic Web Page Segmentation Approaches. In Proceedings of ICWE 2015 (Vol. 9114, pp. 374-391). (LNCS). Springer. https://doi.org/10.1007/978-3-319-19890-3 24
Duivesteijn, W., Feelders, A., & Knobbe, A. (2015). Exceptional Model Mining. Data Mining and Knowledge Discovery, 1-52. https://doi.org/10.1007/s10618-015-0403-4
2014
Scholarly publications
Soons, P., & Feelders, A. (2014). Exploiting Monotonicity Constraints in Active Learning for Ordinal Classification. (Technical Report Series; No. UU-CS-2014-001). UU BETA ICS Departement Informatica.
https://dspace.library.uu.nl/bitstream/handle/1874/306343/2014_001.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/306343/2014_001.pdf?sequence=1
Kreuzer, R., Hage, J., & Feelders, A. (2014). A Quantitative Comparison of Semantic Web Page Segmentation Approaches. (Technical Report Series; No. UU-CS-2014-018). UU BETA ICS Departement Informatica.
https://dspace.library.uu.nl/bitstream/handle/1874/306261/2014_018.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/306261/2014_018.pdf?sequence=1
Woudenberg, S., van der Gaag, L., Feelders, A., & Elbers, A. (2014). Real-Time Adaptive Residual Calculation for Detecting Trend Deviations in Systems with Natural Variability. In Advances in Intelligent Data Analysis XIII (Vol. Cham). (Lecture notes in computer science; Vol. 8819). Springer. https://doi.org/10.1007/978-3-319-12571-8_33
van der Gaag, L., & Feelders, A. (2014). Probabilistic graphical models: 7th European workshop, PGM 2014, Utrecht, The Netherlands, September 17 - 19, 2014 ; proceedings . (Lecture notes in computer science; Vol. 8754). Springer.
Woudenberg, S., van der Gaag, L., Feelders, A., & Elbers, A. (2014). Real-time adaptive problem detection in poultry. In Ecai 2014: 21st european conference on artificial intelligence, 18-22 August 2014, Prague, Czech Republic (pp. 1217-1218). ( Frontiers in Artificial Intelligence and Applications; Vol. 263).. https://doi.org/10.3233/978-1-61499-419-0-1217
Soons, P., & Feelders, A. (2014). Exploiting monotonicity constraints for active learning in ordinal classification. In M. Zaki, Z. Obradovic, P. N. Tan, A. Banerjee, C. Kamath, & S. Parthasarathy (Eds.), Proceedings of the 2014 SIAM International Conference on Data Mining (pp. 659-667). SIAM. https://doi.org/10.1137/1.9781611973440.76
2013
Scholarly publications
Mampaey, M., Nijssen, S., Feelders, A., Konijn, R., & Knobbe, A. (2013). Efficient algorithms for finding optimal binary features in numeric and nominal labeled data. Knowledge and Information Systems, 42(2), 465-492. https://doi.org/10.1007/s10115-013-0714-y
2012
Scholarly publications
van der Gaag, L., Bodlaender, H. L., & Feelders, A. (2012). Monotonicity in Bayesian Networks. CoRR, abs/1207.4160. http://arxiv.org/abs/1207.4160
Roijers, D. M., Jeuring, J. T., & Feelders, A. J. (2012). Probability estimation and a competence model for rule based e-tutoring systems. Department of Information and Computing Sciences, Utrecht University.
Barile, N., & Feelders, A. J. (2012). Active Learning with Monotonicity Constraints. In SIAM International Conference on Data Mining (SDM 2012) (pp. 756-767)
2011
Scholarly publications
Barile, N., & Feelders, A. J. (2011). Monotone Instance Ranking with MIRA. In Proceedings of Discovery Science 2011 (pp. 31-45). Springer.
Pieters, B. F. I., van der Gaag, L. C., & Feelders, A. J. (2011). When Learning Naive Bayesian Classifiers Preserves Monotonicity. In Proceedings of ECSQARU 2011 (pp. 422-433). Springer.
Stegeman, L., & Feelders, A. J. (2011). On generating all optimal monotone classifications. In 11th IEEE International Conference on Data Mining (pp. 685-694)
2010
Scholarly publications
Duivesteijn, W., Knobbe, A. J., Feelders, A. J., & van Leeuwen, M. (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.
Feelders, A. J. (2010). A Decomposition of the Isotonic Regression. Department of Information and Computing Sciences, Utrecht University.
2009
Scholarly publications
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)
van der Gaag, L. C., Renooij, S., Feelders, A. J., de Groote, A. J., 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 (pp. 787-801). Springer. https://doi.org/10.1007/978-3-642-03070-3_59
2008
Scholarly publications
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.
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)
de Knijf, J., & Feelders, A. J. (2008). An Experimental Comparison of Different Inclusion Relations in Frequent Tree Mining. Fundamenta Informaticae, 89(1), 1-22.
Professional publications
Feelders, A. J. (2008). Credit Scoring. In T. Rudas (Ed.), Handbook of probability: theory and applications (pp. 343-362). SAGE.
2006
Scholarly publications
Feelders, A. J., & van der Gaag, L. C. (2006). Learning Bayesian network parameters under order constraints. International Journal of Approximate Reasoning, 42, 37-53.
Feelders, A. J., Velikova, M., & Daniels, H. (2006). Two polynomial algorithms for relabeling non-monotone data. UU WINFI Informatica en Informatiekunde.
https://dspace.library.uu.nl/bitstream/handle/1874/24648/feelders_06_twopolynomial.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/24648/feelders_06_twopolynomial.pdf?sequence=1
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)
Velikova, M., Daniels, H., & Feelders, A. J. (2006). Mixtures of Monotone Networks for Prediction. International Journal of Computational Intelligence, 3, 204-214.
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)
2005
Scholarly publications
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.
Egmont-Petersen, M., Feelders, A. J., & Baesens, B. (2005). Confidence intervals for probabilistic network classifiers. Computational Statistics and Data Analysis, 49(4), 998-1019.
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.
Feelders, A. J., & van der Gaag, L. C. (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). AUAI Press.
Feelders, A. J., & van der Gaag, L. C. (2005). Learning Bayesian Network Parameters Under Order Constraints. (UU-CS ed.) UU WINFI Informatica en Informatiekunde.
https://dspace.library.uu.nl/bitstream/handle/1874/24161/feelders_05_learningbayesiannetwork.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/24161/feelders_05_learningbayesiannetwork.pdf?sequence=1
de Knijf, J., & 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)
2004
Scholarly publications
Feelders, A. J., & van der Gaag, L. C. (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)
2003
Scholarly publications
Feelders, A. J. (2003). Knowledge Discovery in Databases: PKDD 2003. (LNAI ed.) Springer.
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 Springer.
Feelders, A. J. (2003). Statistical Concepts. In M. Berthold, & D. J. Hand (Eds.), Intelligent Data Analysis: an introduction (2nd edition) Springer.
Professional publications
Feelders, A. J. (2003). Reject inference: distinguishing ignorable and non-ignorable selection mechanisms. Credit Risk International, 10-14.
2002
Scholarly publications
Potharst, R., & Feelders, A. J. (2002). Classification Trees for Problems with Monotonicity Constraints. SIGKDD Explorations, 4(1), 1-10.
Feelders, A. J. (2002). Data Mining in Economic Science. In J. Meij (Ed.), Dealing with the data flood (pp. 166-175). (STT; No. 65). STT/Beweton.
Feelders, A. J. (2002). Rule induction by bump hunting. In J. Meij (Ed.), Dealing with the data flood (pp. 697-700). (STT; No. 65). STT/Beweton.
Feelders, A. J. (2002). Clustering. In J. Meij (Ed.), Dealing with the data flood (pp. 629-634). (STT; No. 65). STT/Beweton.
2001
Scholarly publications
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). Springer.
Feelders, A. J., & Daniels, H. A. M. (2001). A general model for automated business diagnosis. European Journal of Operational Research, 130(3), 623-637.