Title: Application of committee kNN classifiers for gene expression profile classification

Authors: Manik Dhawan, Sudarshan Selvaraja, Zhong-Hui Duan

Addresses: Department of Computer Science, University of Akron, Akron, 44325 OH, USA. ' Department of Computer Science, University of Akron, Akron, 44325 OH, USA. ' Department of Computer Science, University of Akron, Akron, 44325 OH, USA

Abstract: In this study, we develop a two-class classification system based on a committee of k-Nearest Neighbour (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 leukaemia patients. We show that the system can be effectively used for classifying microarray gene expression data. The results demonstrate the committee approach consistently outperforms individual kNN classifiers in terms of both classification accuracy and stability.

Keywords: microarray data; gene expression profiles; k-nearest neighbour algorithms; committee of classifiers; ensemble learning; classification stability; classification accuracy; data preprocessing; bioinformatics; leukaemia.

DOI: 10.1504/IJBRA.2010.035998

International Journal of Bioinformatics Research and Applications, 2010 Vol.6 No.4, pp.344 - 352

Published online: 11 Oct 2010 *

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