Constructing ensembles of symbolic classifiers

Issue title: Hybrid Intelligent systems in Ensembles

Article type: Research Article

Authors: Bernardini, Flávia Cristina^{; *} | Monard, Maria Carolina | Prati, Ronaldo C.

Affiliations: Laboratory of Computational Intelligence – LABIC, Institute of Mathematics and Computer Science – ICMC, University of São Paulo – USP, P.O. Box 668, 13560-970, São Carlos, SP, Brazil

Correspondence: [*] Corresponding author. E-mail: [email protected]

Abstract: Learning algorithms are an integral part of the Data Mining (DM) process. However, DM deals with a large amount of data and most learning algorithms do not operate in massive datasets. A technique often used to ease this problem is related to data sampling and the construction of ensembles of classifiers. Several methods to construct such ensembles have been proposed. However, these methods often lack an explanation facility. This work proposes methods to construct ensembles of symbolic classifiers. These ensembles can be further explored in order to explain their decisions to the user. These methods were implemented in the ELE system, also described in this work. Experimental results in two out of three datasets show improvement over all base-classifiers. Moreover, according to the obtained results, methods based on single rule classification might be used to improve the explanation facility of ensembles.

DOI: 10.3233/HIS-2006-3304

Journal: International Journal of Hybrid Intelligent Systems, vol. 3, no. 3, pp. 159-167, 2006