An improved particle swarm optimization for feature selection

作者：Chen Li Fei^*; Su Chao Ton; Chen Kun Huang

来源：Intelligent Data Analysis, 2012, 16(2): 167-182.

DOI：10.3233/IDA-2012-0517

• Feature selection
• particle swarm optimization
• genetic algorithms
• sequential search algorithms

Searching for an optimal feature subset in a high-dimensional feature space is an NP-complete problem; hence, traditional optimization algorithms are inefficient when solving large-scale feature selection problems. Therefore, meta-heuristic algorithms have been extensively adopted to solve the feature selection problem efficiently. This study proposes an improved particle swarm optimization (IPSO) algorithm using the opposite sign test (OST). The test increases population diversity in the PSO mechanism, and avoids local optimal trapping by improving the jump ability of flying particles. Data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification accuracy is employed as a criterion to evaluate classifier performance. Results show that the proposed approach outperforms both genetic algorithms and sequential search algorithms.

• 出版日期2012
• 单位清华大学