Support Vector Based T-Score for Gene Ranking
作者： Piyushkumar A. Mundra , Jagath C. Rajapakse
DOI: 10.1007/978-3-540-88436-1_13
关键词: Computation 、 Feature vector 、 Data mining 、 Standard score 、 Benchmark (computing) 、 Support vector machine 、 Feature (machine learning) 、 Feature selection 、 Ranking SVM 、 Computer science 、 Pattern recognition 、 Artificial intelligence
摘要: T-score between classes and gene expressions is widely used for ranking in microarray expression data analysis. We propose to use only support vector points computation of t-scores ranking. The proposed method uses backward elimination features, similar Support Vector Machine Recursive Feature Elimination (SVM-RFE) formulation, but achieves better results than SVM-RFE t-score based feature selection on three benchmark cancer datasets.
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