Earticle

초록

영어

Classifying cork stopper into group required large set of visual features. Selecting an optimal feature subset from large input feature set speeds up classification task and improve the classifier accuracy. Traditional feature selection methods, such as sequential forward selection, sequential backward selection, and sequential forward floating search are costly to implement. This paper we propose a feature selection method known as principal feature analysis that exploits the structure of the principal components of a feature set to find a subset of the original features information and support vector machines (SVMs) for classification. The experimental result show that the proposed method for SVM based classifier is lot faster than PCA and ICA based methods. It is also leads to better performance when the same number of principal/independent components is used and consistently picks the best subset of features in terms of sum-squared-error compared to competing methods.

목차

Abstract
 1. Introduction
 2. Overview of PCA and ICA
  A. Principal Component Analysis
  B. Independent Component Analysis
 3. The Proposed Method
 4. Support Vector Machine
 5. Experimental Results
 6. Conclusion
 References

키워드

저자

• Aborisade, D.O [ Department of Electronic/Electrical Engineering, Ladoke Akintola University of Technology, Ogbomoso. Oyo-state ]
• Adebayo I.G [ Department of Electronic/Electrical Engineering, Ladoke Akintola University of Technology, Ogbomoso. Oyo-state ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605