In this study, we develop a two-class classification system based on a committee of k-nearest neighbor (kNN) classifiers. The system includes a sequence of simple data preprocessing steps. Each committee consists of 5 kNN classifiers of different architectures. Each classifier on the committee takes in a different set of features. The classification system is then applied to a set of microarray gene expression profiles from leukemia patients. We show that the system can be effectively used for

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... lassifying microarray gene expression data. The results demonstrate the committee approach consistently outperforms individual kNN classifiers in terms of both classification accuracy and stability. Index Terms-microarray gene expression profiles, k-nearest neighbor algorithms, committee of classifiers, stability of classification systems.