Earticle

초록

영어

Feature selection (FS) is a global optimization problem in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable recognition accuracy. It is the most important step that affects the performance of a pattern recognition system. This paper presents a novel feature selection algorithm based on particle swarm optimization (PSO). PSO is a computational paradigm based on the idea of collaborative behavior inspired by the social behavior of bird flocking or fish schooling. The algorithm is applied to coefficients extracted by two feature extraction techniques: the discrete cosine transforms (DCT) and the discrete wavelet transform (DWT). The proposed PSO-based feature selection algorithm is utilized to search the feature space for the optimal feature subset where features are carefully selected according to a well defined discrimination criterion. Evolution is driven by a fitness function defined in terms of maximizing the class separation (scatter index). The classifier performance and the length of selected feature vector are considered for performance evaluation using the ORL face database. Experimental results show that the PSO-based feature selection algorithm was found to generate excellent recognition results with the minimal set of selected features.

목차

Abstract
 1. Introduction
 2. Feature Extraction
  2.1. Discrete Cosine Transform (DCT)
  2.2. Discrete Wavelet Transform (DWT)
 3. Particle Swarm Optimization (PSO)
  3.1. PSO Algorithm
  3.2. Binary PSO and Feature Selection
 4. PSO-Based Feature Selection
  4.1 Chromosome Representation
  4.2 Fitness Function
  4.3 PSO-Based Feature Selection Algorithm
  4.4. Classifier
 5. Experimental Results
  5.1. Experiment 1
  5.2. Experiment 2
 6. Conclusion
 References

키워드

저자

• Rabab M. Ramadan [ Electrical Engineering Department, Faculty of Engineering - Port-Said, Suez Canal University ]
• Rehab F. Abdel - Kader [ Electrical Engineering Department, Faculty of Engineering - Port-Said, Suez Canal University ]

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