J
Jan Vanthienen
Researcher at Katholieke Universiteit Leuven
Publications - 299
Citations - 11665
Jan Vanthienen is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Process mining & Decision table. The author has an hindex of 48, co-authored 291 publications receiving 10299 citations. Previous affiliations of Jan Vanthienen include The Catholic University of America.
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Comprehensible Credit Scoring Models Using Rule Extraction from Support Vector Machines
TL;DR: This paper provides an overview of the recently proposed rule extraction techniques for SVMs and introduces two others taken from the artificial neural networks domain, being Trepan and G-REX, which rank at the top of comprehensible classification techniques.
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An empirical evaluation of the comprehensibility of decision table, tree and rule based predictive models
TL;DR: An empirical study is presented which investigates the suitability of a number of alternative representation formats for classification when interpretability is a key requirement and reveals a clear preference of users for decision tables in terms of ease of use.
Journal ArticleDOI
Comprehensible credit scoring models using rule extraction from support vector machines
TL;DR: In this article, the authors provide an overview of the recently proposed rule extraction techniques for SVMs and introduce two others taken from the artificial neural networks domain, being Trepan and G-REX.
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Bayesian neural network learning for repeat purchase modelling in direct marketing
TL;DR: Experimental evidence is provided that Bayesian neural networks offer a viable alternative for purchase incidence modelling and a combined use of all three RFM predictor categories is advocated by the ARD method.
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A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs
TL;DR: The results of this study indicate that the HeuristicsMiner algorithm is especially suited in a real-life setting, and it is shown that, particularly for highly complex event logs, knowledge discovery from such data sets can become a major problem for traditional process discovery techniques.