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A New Gene Selection Method for Microarray Data Based on PSO and Informativeness Metric

  • Jian Guan
  • Fei Han
  • Shanxiu Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7996)

Abstract

In this paper, a new method encoding a priori information of informativeness metric of microarray data into particle swarm optimization (PSO) is proposed to select informative genes. The informativeness metric is an analysis of variance statistic that represents the regulation hide in the microarray data. In the new method, the informativeness metric is combined with the global searching algorithms PSO to perform gene selection. The genes selected by the new method reveal the data structure highly hided in the microarray data and therefore improve the classification accuracy rate. Experiment results on two microarray datasets achieved by the proposed method verify its effectiveness and efficiency.

Keywords

Gene selection particle swarm optimization informativeness metric extreme learning machine 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Jian Guan
    • 1
  • Fei Han
    • 1
  • Shanxiu Yang
    • 1
  1. 1.School of Computer Science and Telecommunication EngineeringJiangsu UniversityZhenjiangChina

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