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Computational and Mathematical Methods in Medicine
Volume 2018 (2018), Article ID 5490513, 11 pages
Research Article

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

1College of Computer and Information Engineering, Henan Normal University, Xinxiang 453007, China
2Engineering Technology Research Center for Computing Intelligence and Data Mining, Henan Province 453007, China

Correspondence should be addressed to Jiucheng Xu

Received 27 September 2017; Revised 17 December 2017; Accepted 21 December 2017; Published 31 January 2018

Academic Editor: Xiaoqi Zheng

Copyright © 2018 Jiucheng Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The selection of feature genes with high recognition ability from the gene expression profiles has gained great significance in biology. However, most of the existing methods have a high time complexity and poor classification performance. Motivated by this, an effective feature selection method, called supervised locally linear embedding and Spearman’s rank correlation coefficient (SLLE-SC2), is proposed which is based on the concept of locally linear embedding and correlation coefficient algorithms. Supervised locally linear embedding takes into account class label information and improves the classification performance. Furthermore, Spearman’s rank correlation coefficient 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.