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Ensemble Classifier with Negatively Correlated Features for Cancer Classification  

원홍희 (연세대학교 컴퓨터과학과)
조성배 (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Software and Applications / v.30, no.12, 2003 , pp. 1124-1134 More about this Journal
Abstract
The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. It is essential to efficiently analyze DNA microarray data because the amount of DNA microarray data is usually very large. Since accurate classification of cancer is very important issue for treatment of cancer, it is desirable to make a decision by combining the results of various expert classifiers rather than by depending on the result of only one classifier. Generally combining classifiers gives high performance and high confidence. In spite of many advantages of ensemble classifiers, ensemble with mutually error-correlated classifiers has a limit in the performance. In this paper, we propose the ensemble of neural network classifiers learned from negatively correlated features using three benchmark datasets to precisely classify cancer, and systematically evaluate the performances of the proposed method. Experimental results show that the ensemble classifier with negatively correlated features produces the best recognition rate on the three benchmark datasets.
Keywords
DNA microarray; gene expression data; cancer classification; feature selection; classifier; ensemble classifier; negative correlation;
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