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While several conventional techniques for diagnosis of cancer in clinical practice can be often incomplete or misleading, molecular level diagnostics with gene expression profiles can offer the methodology of precise, objective, and systematic cancer classification. Moreover, 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 basis classifiers rather than by deciding the result with 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 to precisely classify cancer, and systematically evaluate the performances of the proposed method using three benchmark datasets. Experimental results show that the ensemble classifier with negatively correlated features produces the best recognition rate on the three benchmark datasets.