J4 ›› 2013, Vol. 10 ›› Issue (2): 231-241.doi: 10.1016/S1672-6529(13)60219-X

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Ying Li, Gang Wang, Huiling Chen, Lian Shi, Lei Qin   

  • 收稿日期:2012-12-28 修回日期:2013-02-28 出版日期:2013-04-06 发布日期:2013-04-10
  • 作者简介: Ying Li, Gang Wang, Huiling Chen, Lian Shi, Lei Qin

An Ant Colony Optimization Based Dimension Reduction Method for High-Dimensional Datasets

Ying Li, Gang Wang, Huiling Chen, Lian Shi, Lei Qin   

  1. 1. College of Computer Science and Technology, Jilin University, Changchun 130012, P. R. China
    2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, P. R. China
    3. College of Geo Exploration Science and Technology, Jilin University, Changchun 130026, P. R. China
    4. College of physics and electronic information, Wenzhou University, Wenzhou 325035, P. R. China
  • Received:2012-12-28 Revised:2013-02-28 Online:2013-04-06 Published:2013-04-10
  • Contact: Gang Wang E-mail:wanggang.jlu@gmail.com
  • About author: Ying Li, Gang Wang, Huiling Chen, Lian Shi, Lei Qin

Abstract:

In this paper, a bionic optimization algorithm based dimension reduction method named Ant Colony Optimization -Selection (ACO-S) is proposed for high-dimensional datasets. Because microarray datasets comprise tens of thousands of features (genes), they are usually used to test the dimension reduction techniques. ACO-S consists of two stages in which two well-known ACO algorithms, namely ant system and ant colony system, are utilized to seek for genes, respectively. In the first stage, a modified ant system is used to filter the nonsignificant genes from high-dimensional space, and a number of promising genes are reserved in the next step. In the second stage, an improved ant colony system is applied to gene selection. In order to enhance the search ability of ACOs, we propose a method for calculating priori available heuristic information and design a fuzzy logic controller to dynamically adjust the number of ants in ant colony system. Furthermore, we devise another fuzzy logic controller to tune the parameter (q0) in ant colony system. We evaluate the performance of ACO-S on five microarray datasets, which have dimensions varying from 7129 to 12000. We also compare the performance of ACO-S with the results obtained from four existing well-known bionic optimization algorithms. The comparison results show that ACO-S has a notable ability to generate a gene subset with the smallest size and salient features while yielding high classification accuracy. The comparative results generated by ACO-S adopting different classifiers are also given. The proposed method is shown to be a promising and effective tool for mining high-dimension data and mobile robot navigation.

Key words: gene selection, feature selection, ant colony optimization, high-dimensional data