Genetics-based machine learning for rule induction: state of the art, taxonomy, and comparative study

Authors:
Alberto Fernández;Salvador García;Julián Luengo;Ester Bernadó-Mansilla;Francisco Herrera
Affiliations:
Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Department of Computer Science, University of Jaén, Jaén, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain;Grup de Recerca en Sistemes Intelligents, Enginyeria i Arquitectura La Salle, Universitat Ramon Llull, Barcelona, Spain;Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain
Venue:
IEEE Transactions on Evolutionary Computation
Year:
2010

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Abstract

The classification problem can be addressed by numerous techniques and algorithms which belong to different paradigms of machine learning. In this paper, we are interested in evolutionary algorithms, the so-called genetics-based machine learning algorithms. In particular, we will focus on evolutionary approaches that evolve a set of rules, i.e., evolutionary rule-based systems, applied to classification tasks, in order to provide a state of the art in this field. This paper has a double aim: to present a taxonomy of the genetics-based machine learning approaches for rule induction, and to develop an empirical analysis both for standard classification and for classification with imbalanced data sets. We also include a comparative study of the genetics-based machine learning (GBML) methods with some classical nonevolutionary algorithms, in order to observe the suitability and high potential of the search performed by evolutionary algorithms and the behavior of the GBML algorithms in contrast to the classical approaches, in terms of classification accuracy.