A new particle swarm feature selection method for classification

Kun-Huang Chen; Li-Fei Chen; Chao-Ton Su

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A new particle swarm feature selection method for classification

机译：分类的新粒子群特征选择方法

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摘要

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.

机译：从高维特征空间中搜索最优特征子集是一个NP完全问题。因此，传统的优化算法在解决大规模特征选择问题时效率低下。因此，广泛采用元启发式算法来有效解决此类问题。本研究针对特征选择问题提出了一种基于回归的粒子群优化算法。所提出的算法可以通过提高飞行粒子的跳跃能力来增加种群多样性并避免局部最优捕获。从UCI机器学习数据库收集的数据集用于评估所提出方法的有效性。分类准确性用作评估分类器性能的标准。结果表明，我们提出的方法优于遗传算法和顺序搜索算法。

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来源
《Journal of Intelligent Information Systems》 |2014年第3期|507-530|共24页
作者
Kun-Huang Chen; Li-Fei Chen; Chao-Ton Su;
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作者单位

Department of Industrial Management, National Taiwan University of Science and Technology, Taipei,Taiwan;

Department of Business Administration, Fu Jen Catholic University, No. 510, Zhongzheng Rd., Xinzhung Dist., New Taipei City 24205, Taiwan, Republic of China;

Department of Industrial Engineering and Engineering Management, National Tsing Hua University, Hsinchu, Taiwan;

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正文语种 eng
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关键词
Feature selection; Particle swarm optimization; Regression; Genetic algorithms; Sequential search algorithms;

机译：功能选择;粒子群优化;回归;遗传算法;顺序搜索算法;

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A new particle swarm feature selection method for classification

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