基于混沌搜索的特征选择方法

doi:10.3969/j.issn.1000-1093.2013.12.019

兵工学报 ›› 2013, Vol. 34 ›› Issue (12): 1616-1619.doi: 10.3969/j.issn.1000-1093.2013.12.019

基于混沌搜索的特征选择方法

申清明^1,2，闫利军^1,2，高建民²，赵静¹

(1.西北机电工程研究所，陕西咸阳 712099；2.西安交通大学机械制造系统工程国家重点实验室，陕西西安 710049)

收稿日期:2013-05-30 修回日期:2013-05-30 上线日期:2014-03-04
作者简介:申清明（1981—），男，工程师
基金资助:
国家高技术研究发展计划项目（2007AA04Z121)

Feature Selection Based on Chaos Search

SHEN Qing-ming^1,2， YAN Li-jun^1,2， GAO Jian-min²， ZHAO Jing¹

(1.Northwest Institute of Mechanical and Electrical Engineering，Xianyang 712099，Shaanxi，China；2.State Key Laboratory for Manufacturing System Engineering，Xi’an Jiaotong University，Xi’an 710049，Shaanxi，China)

Received:2013-05-30 Revised:2013-05-30 Online:2014-03-04

摘要/Abstract

摘要： 针对特征选择效率较低的问题，提出了一种基于混沌搜索的特征选择（CSFS）方法。建立候选特征－混沌变量映射模型，将候选特征映射到混沌空间，实现候选特征向量与混沌变量之间的相互转化；利用混沌变量迭代演化进行特征选择；利用分类器对得到的特征向量进行性能评价。以焊缝缺陷特征为例对该特征选择方法进行了有效性验证，并与基于遗传算法的特征选择（GAFS）方法进行了对比。实验结果表明：在获取的特性向量的识别性能相当的情况下，CSFS方法的耗时仅为GAFS方法的61.1%.

关键词: 系统学, 模式识别, 特征选择, 混沌搜索

Abstract: To improve the efficiency of feature selection, a feature selection method based on chaos search (CSFS) is proposed. Firstly, a mapping model for feature candidates and chaotic variables is established，which maps the feature candidates to the chaos space and realizes the interconversion between them. Secondly, the feature selection is carried out by means of the evolution of the chaotic variable. Finally, the classifier is used to evaluate the obtained feature vector. The features of weld defects are taken for example to verify the proposed method, which is compared with a gene-algorithm-based feature selection (GAFS) method. The experimental results demonstrate that the computation time of CSFS is only 61.1% of that by GAFS in the case of obtaining the feature vectors with the same recognition performance.

Key words: systematics, pattern recognition, feature selection, chaos search

中图分类号:

TN911.7

申清明，闫利军，高建民，赵静. 基于混沌搜索的特征选择方法[J]. 兵工学报, 2013, 34(12): 1616-1619.

SHEN Qing-ming， YAN Li-jun， GAO Jian-min， ZHAO Jing. Feature Selection Based on Chaos Search[J]. Acta Armamentarii, 2013, 34(12): 1616-1619.

参考文献

［1］ Da Silva R R, Calba L P, Siqueira M H S, et al. Pattern recognition of weld defects detected by radiographic test[J]. Ndt & E International, 2004, 37(6): 461-470.
[2] Ekbal A, Saha S. Combining feature selection and classifier ensemble using a multiobjective simulated annealing approach: application to named entity recognition[J]. Soft Computing, 2013, 17 (1): 1-16.
[3] Wang Y, Li L, Ni J, et al. Feature selection using tabu search with long-term memories and probabilistic neural networks[J]. Pattern Recognition Letters, 2009, 30 (7): 661-670.
[4] Kannan A, Jr Maguire G Q, Sharma A, et al. Genetic algorithm based feature selection algorithm for effective intrusion detection in cloud networks[C]∥12th International Conference on Data Mining Workshops. Stockholm：IEEE Conference Publication，2012: 416-423.
[5] Perez M, Marwala T. Microarray data feature selection using hybrid genetic algorithm simulated annealing[C]∥27th Convention of Electrical & Electronics Engineers in Israel. Johannesburg：IEEE Conference Publication，2012: 1-5.
[6] Ammaruekarat P, Meesad P. A multi-objective memetic algorithm based on chaos optimization[J]. Applied Mechanics and Materials, 2012, 130 (2012): 725-729.
[7] Wen Z B, Yue Y X, Yue Q X. Chaos optimization algorithm for vehicle routing problem[J]. Advanced Materials Research, 2012, 538 (2012)：2722-2726.
[8] Tavazoei M S，Haeri M. An optimization algorithm based on chaotic behavior and fractal nature[J]. Journal of Computational and Applied Mathematics, 2007, 206 (2): 1070-1081.
[9] Liao G C, Tsao T P. Application embedded chaos search immune genetic algorithm for short-term unit commitment[J]. Electric Power Systems Research, 2004, 71 (2): 135-144.
[10] Shen Q M, Gao J M, Li C. Automatic classification of weld defects in radiographic images[J]. Insight, 2010, 52 (3): 134-139.

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基于混沌搜索的特征选择方法

Feature Selection Based on Chaos Search

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