Earticle

초록

영어

In this paper, a new method based on Particle Swarm Optimization (PSO) for independent component analysis(ICA) is presented which can be applied for feature extraction. Due to the drawbacks of the Gradient method, it is replaced by PSO in Discriminant Independent Component Analysis (dICA ) algorithm in the proposed approach. The Gradient method may lead to local optimal and it cannot solve the problem of slow convergence since it includes a learning step which needs to be determined in advance. Moreover, Gradient-based techniques cannot achieve high level of accuracy because of the considerable complexity involved in ICA. The additional complexity of the Gradient-based algorithms leads to pseudo-optimal scenarios. The Discriminant Independent Component Analysis based on PSO is used to overcome these serious shortcomings. Most of the datasets used for simulation in this study are obtained from UCI repository. The results obtained using linear discriminant analysis (LDA), principal component analysis (PCA) and gradient-based dICA are compared with those obtained by PSO-dICA . The results show improvement in classification with PSO-dICA method compared to other methods. In other words, PSO-dICA method bought about classifier error reduction.

목차

Abstract
 1. Introduction
 2. Particle Swarm Optimization
 3. Independent Component Analysis
 4. Discriminant Independent Component Analysis
 5. Improved Method using PSO-dICA
 6. Simulation Result
  6.1. Experimental results of the proposed method.
 7. Conclusion
 References

키워드

저자

• Maryam Mollaee [ Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran ]
• Mohammad Hossein Moattar [ Department of Software Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran ]
• Seyyed Javad Seyyed Mahdavi [ Department of Artificial Intelligence engineering, Mashhad Branch, Islamic AzadUniversity, Mashhad, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605