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An ensemble of classifiers with genetic algorithmBased Feature Selection

Zhang, Zili and Yang, Pengyi 2008, An ensemble of classifiers with genetic algorithmBased Feature Selection, The IEEE intelligent informatics bulletin, vol. 9, no. 1, pp. 18-24.

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Title An ensemble of classifiers with genetic algorithmBased Feature Selection
Author(s) Zhang, Zili
Yang, Pengyi
Journal name The IEEE intelligent informatics bulletin
Volume number 9
Issue number 1
Start page 18
End page 24
Publisher IEEE
Place of publication Washington, D.C.
Publication date 2008-11
ISSN 1727-5997
1727-6004
Keyword(s) ensemble classifiers
multi-objective genetic algorithms
decision tree
artificial neural networks
support vector machines
Summary Different data classification algorithms have been developed and applied in various areas to analyze and extract valuable information and patterns from large datasets with noise and missing values. However, none of them could consistently perform well over all datasets. To this end, ensemble methods have been suggested as the promising measures. This paper proposes a novel hybrid algorithm, which is the combination of a multi-objective Genetic Algorithm (GA) and an ensemble classifier. While the ensemble classifier, which consists of a decision tree classifier, an Artificial Neural Network (ANN) classifier, and a Support Vector Machine (SVM) classifier, is used as the classification committee, the multi-objective Genetic Algorithm is employed as the feature selector to facilitate the ensemble classifier to improve the overall sample classification accuracy while also identifying the most important features in the dataset of interest. The proposed GA-Ensemble method is tested on three benchmark datasets, and compared with each individual classifier as well as the methods based on mutual information theory, bagging and boosting. The results suggest that this GA-Ensemble method outperform other algorithms in comparison, and be a useful method for classification and feature selection problems.
Notes This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 899999 Information and Communication Services not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2008
Copyright notice ©2008, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30017964

Document type: Journal Article
Collections: School of Engineering and Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.