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Open AccessArticle

Multi-Objective Evolutionary Rule-Based Classification with Categorical Data

Department of Information and Communication Engineering, University of Murcia, 30071 Murcia, Spain
Centre for Applied Data Analytics Research (CeADAR), University College Dublin, D04 Dublin 4, Ireland
Department of Mathematics and Computer Science, University of Ferrara, 44121 Ferrara, Italy
Author to whom correspondence should be addressed.
Entropy 2018, 20(9), 684;
Received: 30 July 2018 / Revised: 3 September 2018 / Accepted: 6 September 2018 / Published: 7 September 2018
(This article belongs to the Special Issue Statistical Machine Learning for Human Behaviour Analysis)
The ease of interpretation of a classification model is essential for the task of validating it. Sometimes it is required to clearly explain the classification process of a model’s predictions. Models which are inherently easier to interpret can be effortlessly related to the context of the problem, and their predictions can be, if necessary, ethically and legally evaluated. In this paper, we propose a novel method to generate rule-based classifiers from categorical data that can be readily interpreted. Classifiers are generated using a multi-objective optimization approach focusing on two main objectives: maximizing the performance of the learned classifier and minimizing its number of rules. The multi-objective evolutionary algorithms ENORA and NSGA-II have been adapted to optimize the performance of the classifier based on three different machine learning metrics: accuracy, area under the ROC curve, and root mean square error. We have extensively compared the generated classifiers using our proposed method with classifiers generated using classical methods such as PART, JRip, OneR and ZeroR. The experiments have been conducted in full training mode, in 10-fold cross-validation mode, and in train/test splitting mode. To make results reproducible, we have used the well-known and publicly available datasets Breast Cancer, Monk’s Problem 2, Tic-Tac-Toe-Endgame, Car, kr-vs-kp and Nursery. After performing an exhaustive statistical test on our results, we conclude that the proposed method is able to generate highly accurate and easy to interpret classification models. View Full-Text
Keywords: multi-objective evolutionary algorithms; rule-based classifiers; interpretable machine learning; categorical data multi-objective evolutionary algorithms; rule-based classifiers; interpretable machine learning; categorical data
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MDPI and ACS Style

Jiménez, F.; Martínez, C.; Miralles-Pechuán, L.; Sánchez, G.; Sciavicco, G. Multi-Objective Evolutionary Rule-Based Classification with Categorical Data. Entropy 2018, 20, 684.

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