Earticle

초록

영어

Feature selection is an important data preprocessing technique and has been widely studied in data mining, machine learning and granular computing. In this paper, we introduced an effective feature selection method using the hybrid approaches, that is, use the mutual information to select the candidate feature set, then, obtain the super-reduct space from the candidate feature set by a wrapper search approach, finally, the wrapper method determined to select the proper features. The experimental results show that our approach owned the obvious merits in the aspect of classification accuracy ratio and number features selected by extensive comparing with other methods.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Course of Feature Selection
  3.1 Find a Candidate Feature Set
  3.2 Select the Best Superreduct
  3.3 Selecting the Best Feature Subset on Wrapper Approach
 4. Experimental Results and Analyze
 5. Conclusion
 References

키워드

저자

• Haitao Wang [ School of Computer Science and Technology Henan Polytechnic University GaoXin District, JiaoZuo City, HeNan Province, China ]
• Shufen Liu [ School of Computer Science and Technology JiLin University QianJin Street, ChangChun City, JinLin Province, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605