期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2013
卷号：3
期号：3
页码：155-159
出版社：International Journal of Soft Computing & Engineering
摘要：Feature selection aims at finding the most relevant features of a problem domain. However, identification of useful features from hundreds or even thousands of related features is a nontrivial task. This paper is aimed at identifying a small set of genes, to improving computational speed and prediction accuracy; hence we have proposed a three-stage of gene selection algorithm for microarray data. The proposed approach combines information gain (IG), Significance Analysis for Microarrays (SAM), mRMR (Minimum Redundancy Maximum Relevance) and Support Vector Machine Recursive Feature Elimination (SVM-RFE). In the first stage, intersection part of feature sets is identified by applying the (SAM–IG). While, the second minimizes the redundancy with the help of mRMR method, which facilitates the selection of effectual gene subset from intersection part that recommended from the first stage. In the third stage, (SVM-RFE) is applied to choose the most discriminating genes. We evaluated our technique on AML and ALL (leukemia) dataset using Support Vector Machines (SVM- RBF) classifier, and show the potentiality of the proposed method with the advantage of improving the classification performance.
