A new particle swarm feature selection method for classification

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

来源：Journal of Intelligent Information Systems, 2014, 42(3): 507-530.

DOI：10.1007/s10844-013-0295-y

• Feature selection
• Particle swarm optimization
• Regression
• Genetic algorithms
• Sequential search algorithms

Searching for an optimal feature subset from 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 are extensively adopted to solve such problems efficiently. This study proposes a regression-based particle swarm optimization for feature selection problem. The proposed algorithm can increase population diversity and avoid local optimal trapping by improving the jump ability of flying particles. The data sets collected from UCI machine learning databases are used to evaluate the effectiveness of the proposed approach. Classification accuracy is used as a criterion to evaluate classifier performance. Results show that our proposed approach outperforms both genetic algorithms and sequential search algorithms.

• 出版日期2014-6
• 单位清华大学