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

Multi-stage dynamic ensemble selection using heterogeneous learning algorithms: application on... 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 IFN­ENIT 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. Reference to this paper should be made as follows: Azizi, N., Farah, N. and Khadir, M.T. (2015) ` http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Knowledge Management Studies Inderscience Publishers

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

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Publisher
Inderscience Publishers
Copyright
Copyright © 2015 Inderscience Enterprises Ltd.
ISSN
1743-8268
eISSN
1743-8276
DOI
10.1504/IJKMS.2015.071648
Publisher site
See Article on Publisher Site

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 IFN­ENIT 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. Reference to this paper should be made as follows: Azizi, N., Farah, N. and Khadir, M.T. (2015) `

Journal

International Journal of Knowledge Management StudiesInderscience Publishers

Published: Jan 1, 2015

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