Title: Multi-stage dynamic ensemble selection using heterogeneous learning algorithms: application on classification problems

Authors: Nabiha Azizi; Nadir Farah; Mohamed Tarek Khadir

Addresses: Labged Laboratory, Computer Science Department, Badji Mokhtar University of Annaba, Sidi Ammar, PO BOX 12, Annaba 23000, Algeria ' Labged Laboratory, Computer Science Department, Badji Mokhtar University of Annaba, Sidi Ammar, PO BOX 12, Annaba 23000, Algeria ' Labged Laboratory, Computer Science Department, Badji Mokhtar University of Annaba, Sidi Ammar, PO BOX 12, Annaba 23000, Algeria

Abstract: Classification is one of the most popular and significant machine learning research focuses. It particularly takes paramount importance when a data repository contains samples that can be used as the basis for future decision making. To improve classification accuracy in complex application domains, there has been a growing research activity in the study of efficient methods to construct classifier sets (or multi-classifiers approaches) by combining the results of several classifiers. For pattern classification, dynamic ensemble learning methods explore the use of different classifiers for different samples, therefore, obtaining better generalisation abilities than static ensemble learning methods. This paper introduces a new dynamic selection of learning algorithm based on competence and results of output classes classifier and entropy diversity measure. Obtained performances are compared to the ones of six multiple classifiers systems, using data sets taken from the UCI Machine Learning Repository and IFN-ENIT database. The proposed approach outperformed the benchmark systems in terms of classification accuracies regardless of the type of used classifiers.

Keywords: machine learning; ensemble classifier construction; dynamic classifier selection; diversity measures; classifier fusion; heterogeneous learning algorithms; classification accuracy; multi-stage ensemble selection; pattern classification.

DOI: 10.1504/IJKMS.2015.071648

International Journal of Knowledge Management Studies, 2015 Vol.6 No.1, pp.16 - 30

Available online: 09 Sep 2015 *

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