Classification consistency analysis for bootstrapping gene selection

Authors:
Shaoning Pang;Ilkka Havukkala;Yingjie Hu;Nikola Kasabov
Affiliations:
Auckland University of Technology, Knowledge Engineering and Discovery Research Institute, Private Bag 92006, 1020, Auckland, New Zealand;Auckland University of Technology, Knowledge Engineering and Discovery Research Institute, Private Bag 92006, 1020, Auckland, New Zealand;Auckland University of Technology, Knowledge Engineering and Discovery Research Institute, Private Bag 92006, 1020, Auckland, New Zealand;Auckland University of Technology, Knowledge Engineering and Discovery Research Institute, Private Bag 92006, 1020, Auckland, New Zealand
Venue:
Neural Computing and Applications
Year:
2007

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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.