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Diversity based Ensemble Genetic Programming for Improving Classification Performance  

Hong Jin-Hyuk (연세대학교 컴퓨터과학과)
Cho Sung-Bae (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Software and Applications / v.32, no.12, 2005 , pp. 1229-1237 More about this Journal
Abstract
Combining multiple classifiers has been actively exploited to improve classification performance. It is required to construct a pool of accurate and diverse base classifier for obtaining a good ensemble classifier. Conventionally ensemble learning techniques such as bagging and boosting have been used and the diversify of base classifiers for the training set has been estimated, but there are some limitations in classifying gene expression profiles since only a few training samples are available. This paper proposes an ensemble technique that analyzes the diversity of classification rules obtained by genetic programming. Genetic programming generates interpretable rules, and a sample is classified by combining the most diverse set of rules. We have applied the proposed method to cancer classification with gene expression profiles. Experiments on lymphoma cancer dataset, prostate cancer dataset and ovarian cancer dataset have illustrated the usefulness of the proposed method. h higher classification accuracy has been obtained with the proposed method than without considering diversity. It has been also confirmed that the diversity increases classification performance.
Keywords
genetic programming; ensemble; diversity; cancer classification;
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Times Cited By KSCI : 1  (Citation Analysis)
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