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2020 | OriginalPaper | Chapter

Towards Selecting Reducts for Building Decision Rules for Rule-Based Classifiers

Authors : Manuel S. Lazo-Cortés, José Fco. Martínez-Trinidad, Jesús A. Carrasco-Ochoa, Nelva N. Almanza-Ortega

Published in: Pattern Recognition

Publisher: Springer International Publishing

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Abstract

In rule-based classifiers, calculating all possible rules of a learning sample consumes many resources due to its exponential complexity. Therefore, finding ways to reduce the number and length of the rules without affecting the efficacy of a classifier remains an interesting problem. Reducts from rough set theory have been used to build rule-based classifiers by their conciseness and understanding. However, the accuracy of the classifiers based on these rules depends on the selected rule subset. In this work, we focus on analyzing three different options for using reducts for building decision rules for rule-based classifiers .

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Title: Towards Selecting Reducts for Building Decision Rules for Rule-Based Classifiers
Authors: Manuel S. Lazo-Cortés
José Fco. Martínez-Trinidad
Jesús A. Carrasco-Ochoa
Nelva N. Almanza-Ortega
Publisher: Springer International Publishing
Book: Pattern Recognition
Print ISBN: 978-3-030-49075-1

Electronic ISBN: 978-3-030-49076-8

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-49076-8_7

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