DNA gene expression classification with ensemble classifiers optimized by speciated genetic algorithm

Authors:
Kyung-Joong Kim;Sung-Bae Cho
Affiliations:
Department of Computer Science, Yonsei University, Seoul, South Korea;Department of Computer Science, Yonsei University, Seoul, South Korea
Venue:
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
Year:
2005

Citing 2
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Dataset complexity in gene expression based cancer classification using ensembles of k-nearest neighbors

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Abstract

Accurate cancer classification is very important to cancer diagnosis and treatment. As molecular information is increasing for the cancer classification, a lot of techniques have been proposed and utilized to classify and predict the cancers from gene expression profiles. In this paper, we propose a method based on speciated evolution for the cancer classification. The optimal combination among several feature-classifier pairs from the various features and classifiers is evolutionarily searched using the deterministic crowding genetic algorithm. Experimental results demonstrate that the proposed method is more effective than the standard genetic algorithm and the fitness sharing genetic algorithm as well as the best single classifier to search the optimal ensembles for the cancer classification.