Gene expression classification using optimal feature/classifier ensemble with negative correlation

In order to predict the cancer class of patients, we illustrate a classification framework that combines sets of classifiers trained with independent two features. We suggest an ensemble classifier that is composed of multiple classifiers. Experimental results show that the feature sets that have negative or non-positive correlations produces very high recognition result.