Perturbation and candidate analysis to combat overfitting of gene expression microarray data
by Ravi Mathur; J. David Schaffer; Walker H. Land Jr.; John J. Heine; Jonathan M. Hernandez; Timothy Yeatman
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 4, No. 4, 2011

Abstract: Analysis of gene expression microarray datasets presents the high risk of over-fitting (spurious patterns) because of their feature-rich but case-poor nature. This paper describes our ongoing efforts to develop a method to combat over-fitting and determine the strongest signal in the dataset. A GA-SVM hybrid along with Gaussian noise (manual noise gain) is used to discover feature sets of minimal size that accurately classifies the cases under cross-validation. Initial results on a colorectal cancer dataset shows that the strongest signal (modest number of candidates) can be found by a binary search.

Online publication date: Sat, 24-Jan-2015

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