Journal of Beijing University of Posts and Telecommunications

  • EI核心期刊

JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM ›› 2016, Vol. 39 ›› Issue (s1): 72-75.doi: 10.13190/j.jbupt.2016.s.017

• Papers • Previous Articles     Next Articles

Sample Weighting Based Gene Feature Selection Model

RUI Lan-lan, ZHANG Jie, GUO Shao-yong, XIONG Ao   

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2015-06-06 Online:2016-06-28 Published:2016-06-28

Abstract:

According to the characteristics of gene expression data, a gene feature selection model based on improved information gain was put forward. The improved information gain was proposed to measure gene information quantity with sample weight and a no de-noising and de-noising gene feature selection model was established. The proposed model is compared with common gene selection model using four classifiers. Experiments validate that the proposed method can improve stability of feature selection algorithms without sacrificing predictive accuracy.

Key words: feature selection, information gain, sample weight, noise interference

CLC Number: