T-score, based on t-statistics between samples and disease classes, is a widely used filter criterion for gene selection from microarray data. However, classical T-score uses all the training samples but for both biological and computational reasons, selection of relevant samples for training is an important step in classification. Using a modified logistic regression approach, we propose a sample selection criterion based on T-score and develop a backward elimination approach for gene selection. The method is more stable and computationally less costly compared to support vector machine recursive feature elimination (SVM-RFE) methods. © 2013 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Mundra, P. A., Rajapakse, J. C., & Maduranga, D. A. K. (2013). Simultaneous sample and gene selection using t-score and approximate support vectors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7986 LNBI, pp. 79–90). Springer Verlag. https://doi.org/10.1007/978-3-642-39159-0_8
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