Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring

By David Martens, Johan Huysmans, Rudy Setiono, Jan Vanthienen, Bart Baesens. 2008

99 citations recorded by the source corpus.

Subfields: Accounting, Artificial Intelligence, Computational Theory and Mathematics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

A Diagnostic Study of Explainability Techniques for Text Classification (2020, methodology): “Such post-hoc explanation techniques can be applicable to one speciﬁc model (Martens et al., 2008; Wagner et al., 2019) or to a broader range thereof (Ribeiro et al., 2016; Lundberg and Lee, 2017).”
Interpretability of machine learning‐based prediction models in healthcare (2020, methodology): “Traditionally the focus in ML research was on global interpretability in an effort to help users understand the relationship between all possible inputs to a ML model and the space of all predictions made by the model (Bratko, 1999; Martens, et al., 2008).”
Hybrid support vector machine rule extraction method for discovering the preferences of stock market investors: Evidence from Montenegro (2015, methodology): “The application of rule induction techniques to clean datasets leads to higher accuracy (Martens et al., 2008).”
Rule extraction from support vector machines: A review (2010, methodology): “The survey conceptually groups and analyzes the literature at a higher level of abstraction, which outlines differing fundamental approaches to the topic, rather than summarizing the main features of individual algorithms, as in [37,49].”
Explanation and Justification in Machine Learning : A Survey Or (2017, methodology): “[Martens et al., 2008] proposed to interpret the predictions of an SVM classifier by extracting conjunctive rules using a small subset of features.”
ForEx++: A New Framework for Knowledge Discovery from Decision Forests (2017, methodology): “Extraction of rules from different types of classifiers is a popular area of research; in recent years a number of rule extraction methods have been proposed (Mashayekhi and Gras (2015), Martens et al. (2008), Schmitz et al. (1999)).”
A Survey on the Explainability of Supervised Machine Learning (2020, methodology): “Minerva (Martens et al., 2008) is a sequential covering algorithm that uses iterative growing to extract decision rules from a black box.”