Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification

The selection of feature genes with high recognition ability from the gene expression profles has gained great signifcance in biology. However, most of the existing methods have a high time complexity and poor classifcation performance. Motivated by this, an efective feature selection method, called supervised locally linear embedding and Spearman’s rank correlation coefcient (SLLESC2 ), is proposed which is based on the concept of locally linear embedding and correlation coefcient algorithms. Supervised locally linear embedding takes into account class label information and improves the classifcation performance. Furthermore, Spearman’s rank correlation coefcient is used to remove the coexpression genes. The experiment results obtained on four public tumor microarray datasets illustrate that our method is valid and feasible.