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Advances in Rough and Soft Clustering: Meta-Clustering, Dynamic Clustering, Data-Stream Clustering Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Multi-objective Search for Comprehensible Rule Ensembles

Authors : Jerzy Błaszczyński, Bartosz Prusak, Roman Słowiński

Published in: Rough Sets

Publisher: Springer International Publishing

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Abstract

We present a methodology for constructing an ensemble of rule base classifiers characterized not only by a good accuracy of classification but also by a good quality of knowledge representation. The base classifiers forming the ensemble are composed of minimal sets of rules that cover training objects, while being relevant for their high support, low anti-support and high Bayesian confirmation measure. The population of base classifiers is evolving in course of a bi-objective optimization procedure that involves accuracy of classification and diversity of base classifiers. The final population constitutes an ensemble classifier enjoying some desirable properties, as shown in a computational experiment.

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Metadata
Title
Multi-objective Search for Comprehensible Rule Ensembles
Authors
Jerzy Błaszczyński
Bartosz Prusak
Roman Słowiński
Copyright Year
2016
Book
Rough Sets

Print ISBN: 978-3-319-47159-4

Electronic ISBN: 978-3-319-47160-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-47160-0_46

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