An important application of microarrays is to identify the relevant genes, among thousands of genes, for phenotypic classification. The performance of a gene selection algorithm is often assessed in terms of both predictive capacity and computational efficiency, but predictive capacity of selected features receives more attention than does computational efficiency. However, in gene selection problems, the computational efficiency is equally important because of very high dimensionality of gene expression data. We propose an SVM-IRFS algorithm which combines Support Vector Machine (SVM) based criterion, generalized parwpar2measure, with a new search procedure, named as Iterative Reduced Forward Selection (IRFS), to address the gene selection problem. In the IRFS, an adaptive threshold is used to screen the irrelevant feature subsets, thus unnecessary computations can be avoided. The advantage of our proposed SVM-IRFS algorithm is twofold. First, the selection procedure of SVM-IRFS algorithm is computationally very efficient. It can identify tens from thousands of genes in several seconds. Second, benefiting from the good classification performance of support vector machines, SVM-IRFS produces the feature subset with high predictive capacity.

Abstract Motivation: Given the thousands of genes and the small number of samples, gene selection has emerged as an important research problem in microarray data analysis. Support Vector Machine—Recursive Feature Elimination (SVM-RFE) is one of a group of recently described algorithms which represent the stat-of-the-art for gene selection. Just like SVM itself, SVM-RFE was originally designed to solve binary gene selection problems. Several groups have extended SVM-RFE to solve multiclass problems using one-versus-all techniques. However, the genes selected from one binary gene selection problem may reduce the classification performance in other binary problems. Results: In the present study, we propose a family of four extensions to SVM-RFE (called MSVM-RFE) to solve the multiclass gene selection problem, based on different frameworks of multiclass SVMs. By simultaneously considering all classes during the gene selection stages, our proposed extensions identify genes leading to more accurate classification. Contact: [email protected] Supplementary information: Supplementary materials, including a detailed review of both binary and multiclass SVMs, and complete experimental results, are available at Bioinformatics online.

Bioinformatics (2007) 23(9), 1106–1114 The authors would like to apologize for errors in the equations related to Taylor series. The corrections are shown below. The first sentence beneath Equation (7) should be: At the optimum of J, the first-order term can be neglected1, then Equation (7) becomes ⁠. The first sentence beneath Equation (11) should be: At the optimum of J, ⁠.