Classification consistency analysis for bootstrapping gene selection

Shaoning Pang1 , Ilkka Havukkala1 , Yingjie Hu1 and Nikola Kasabov1

(1) Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Private Bag 92006, Auckland, 1020, New Zealand

Received: 22 November 2006 Accepted: 6 March 2007 Published online: 30 March 2007

Abstract Consistency modelling for gene selection is a new topic emerging from recent cancer bioinformatics research. The result of operations such as classification, clustering, or gene selection on a training set is often found to be very different from the same operations on a testing set, presenting a serious consistency problem. In practice, the inconsistency of microarray datasets prevents many typical gene selection methods working properly for cancer diagnosis and prognosis. In an attempt to deal with this problem, this paper proposes a new concept of classification consistency and applies it for microarray gene selection problem using a bootstrapping approach, with encouraging results.