Fast leave-one-out evaluation for dynamic gene selection

Gene selection procedure is a necessary step to increase the accuracy of machine learning algorithms that help in disease diagnosis based on gene expression data. This is commonly known as a feature subset selection problem in machine learning domain. A fast leave-one-out (LOO) evaluation formula for least-squares support vector machines (LSSVMs) is introduced here that can guide our backward feature selection process. Based on that, we propose a fast LOO guided feature selection (LGFS) algorithm. The gene selection step size is dynamically adjusted according to the LOO accuracy estimation. For our experiments, the application of LGFS to the gene selection process improves the classifier accuracy and reduces the number of features required as well. The least number of genes that can maximize the disease classification accuracy is automatically determined by our algorithm.