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Accurate classification of DNA microarray data is vital for cancer diagnosis and treatment. For greater accuracy, a preferable strategy is to make a decision based on the result of a single classifier that is trained with various aspects of data space. It is a difficult task to create an optimal classifier for DNA analysis that deals with only a few samples with large number of features. Usually, different feature sets are provided for classifiers to learn. If the feature sets provide similar information, the classifiers trained from them cannot improve the performance because they will make the same error and there is no possibility of compensation. In this paper, we adopt correlation analysis of feature selection methods as a guideline for selection of features for classifiers to learn. We use a negative correlation method for generation of feature sets those are mutually exclusive. Each classifier is learned from different features sets based on correlation analysis to classify cancer precisely. In this way, we evaluated the performance with two benchmark datasets. Experimental results show that classifiers, which have learned from different feature sets that are negatively correlated with each other, produce the best recognition rates on the two benchmark datasets.