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• W2115057231 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." @default.
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• W2115057231 date "2011-01-01" @default.
• W2115057231 modified "2023-09-27" @default.
• W2115057231 title "Perturbation and candidate analysis to combat overfitting of gene expression microarray data" @default.
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• W2115057231 doi "https://doi.org/10.1504/ijcbdd.2011.044443" @default.
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