基于多目标微粒群优化的异质数据特征选择

doi:10.3969/j.issn.0372-2112.2014.07.012

电子学报 ›› 2014, Vol. 42 ›› Issue (7): 1320-1326.DOI: 10.3969/j.issn.0372-2112.2014.07.012

基于多目标微粒群优化的异质数据特征选择

巩敦卫, 胡滢, 张勇

中国矿业大学信息与电气工程学院, 江苏徐州 221116

收稿日期:2013-04-15 修回日期:2013-05-17 出版日期:2014-07-25
- 作者简介:
- 巩敦卫 男，1970年3月出生，江苏铜山人，中国矿业大学教授、博导、中国电子学会高级会员、中国煤炭学会高级会员.1992年、1995年和1999年分别在中国矿业大学、北京航空航天大学、中国矿业大学获理学学士、工学硕士和工学博士学位.主要研究方向：基于搜索的软件工程、智能优化与控制.E-mail：dwgong@vip.163.com；胡滢女，1988年4月出生，安徽黄山人.2011年在安徽理工大学获工学学士学位，现为中国矿业大学硕士研究生，主要研究方向：基于微粒群优化的特征选择.E-mail：hy200712008@126.com；张勇男，1979年9月出生，山东莱芜人，中国矿业大学副教授.2003年在聊城大学获工学学士学位，2006年和2009年在中国矿业大学分别获工学硕士和工学博士学位.主要研究方向：微粒群优化及其应用.E-mail：yongzh401@126.com
- 基金资助:
- 国家自然科学基金 (No.61005089）; 江苏省自然科学基金 (No.BK2011215）; 高等学校博士学科点专项科研基金 (No.20100095120016）; 中国博士后科学基金 (No.2012M521142）

Feature Selection of Heterogeneous Data Based on Multi-Objective Particle Swarm Optimization

GONG Dun-wei, HU Ying, ZHANG Yong

School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China

Received:2013-04-15 Revised:2013-05-17 Online:2014-07-25 Published:2014-07-25
- Supported by:
- National Natural Science Foundation of China (No.61005089); Natural Science Foundation of Jiangsu Province, China (No.BK2011215); Research Fund for the Doctoral Program of Higher Education of China (No.20100095120016); Postdoctoral Science Foundation of China (No.2012M521142)

摘要/Abstract

摘要：

环境和测量仪器精度的影响，使得采样数据的不同特征具有不同的质量.对这类异质数据进行特征选择，需要同时考虑特征子集确定分类器的准确度和可靠性，从而增加了特征选择的难度.本文研究异质数据的特征选择问题，提出一种基于多目标微粒群优化的特征选择方法.该方法首先以特征选择的概率为决策变量，将具有离散变量的特征选择问题，转化为连续变量多目标优化问题；然后，采用微粒群优化求解时，基于高斯采样，产生微粒的全局引导者，以提高Pareto解集的分布性；最后，依据储备集中元素更新的速度，确定需要扰动的微粒，以帮助微粒群跳出局部最优.将所提方法应用于多个典型数据集分类问题，实验结果表明了所提方法的有效性.

关键词: 特征选择, 异质数据, 多目标优化, 微粒群优化, 高斯采样

Abstract:

Different features of a sampling datum have different quality as a result the influence of the environment and the equipment precision.For the feature selection of this kind of heterogeneous data,both the accuracy and the reliability of the classifier determined by a feature subset are required to simultaneously consider,which enhances the difficulty of selecting features.The problem of the feature selection of heterogeneous data is focused on in this paper,and a method of selecting features is presented based on multi-objective particle swarm optimization.In this method,the above problem is first converted to a multi-objective optimization problem by regarding the probability of selecting a feature as the decision variable.When particle swarm optimization (PSO) is employed to solve the converted problem,the global guider of particles is generated by Gaussian sampling so as to improve the performance of Pareto solutions in distribution.In addition,the particle to be disturbed is determined according to the speed of updating a particle in the archive to help the swarm jump out of local optima.The proposed method is applied to classify several benchmark data sets,and the experimental results demonstrate its effectiveness.

Key words: feature selection, heterogeneous data, multi-objective optimization, particle swarm optimization, Gaussian sampling

中图分类号:

TP301

巩敦卫, 胡滢, 张勇. 基于多目标微粒群优化的异质数据特征选择[J]. 电子学报, 2014, 42(7): 1320-1326.

GONG Dun-wei, HU Ying, ZHANG Yong . Feature Selection of Heterogeneous Data Based on Multi-Objective Particle Swarm Optimization[J]. Acta Electronica Sinica, 2014, 42(7): 1320-1326.

参考文献

[1] Roberto H W,George D C,Renato F C,et al.A global-ranking local feature selection method for text categorization[J].Expert System with Applications,2012,39(17):12851-12857.
[2] 朱颢东，钟勇.基于并行二进制免疫量子粒子群优化的特征选择方法[J].控制与决策,2010,25(1):53-63. Zhu Ying-dong,Zhong Yong.Feature selection method based on PBIQPSO[J].Control and Decision,2010,25(1):53-63.(in Chinese)
[3] Peng S H,Xu Q H,Ling X B,et al.Molecular classification of cancer types from micro array data using the combination of genetic algorithms and support vector machines[J].FEBS Letters,2003,555(2):358-362.
[4] Chuang L Y,Yang C H,Li J C.Chaotic maps based on binary particle swarm optimization for feature selection[J].Applied Soft Computing,2011,11(1):239-248.
[5] 宋炜，刘强.基于模拟退火算法的过程挖掘研究[J].电子学报,2009,37(S1):135-139. Song Wei,Liu Qiang.Business process mining based on simulated annealing[J].Acta Electronica Sinica,2009,37(S1):135-139.(in Chinese)
[6] 张昊，陶然，李志勇，蔡镇河.基于KNN算法及禁忌搜索算法的特征选择方法在入侵检测中的应用研究[J].电子学报,2009,37(7):1628-1632. Zhang Hao,Tao Ran,Li Zhi-yong,et al.A research and application of feature selection based on KNN and tabu search algorithms in the intrusion detection[J].Acta Electronica Sinica,2009,37(7):1628-1632.(in Chinese)
[7] 李国辉，冯明月，易先清.基于分群粒子群优化的传感器调度方法[J].系统工程与电子技术,2010,32(3):598-602. Li Guo-hui,Feng Ming-yue,Yi Xian-qing.Sensor scheduling method based on grouping particle swarm optimization[J].Journal of Systems Engineering and Electronics,2010,32(3):598-602.(in Chinese)
[8] Zhang W,Liu Y T.Multi-objective reactive power and voltage control based on fuzzy optimization strategy and fuzzy adaptive particle swarm[J].Journey of Electrical Power and Energy Systems,2008,30(9):525-532.
[9] Moslehi G,Mahnam M.A Pareto approach to multi-objective flexible job-shop scheduling problem using particle swarm optimization and local search[J].International Journal of Production Economics,2011,129(1):14-22.
[10] Mohammad J A,Davar G.Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules[J].Engineering Applications of Artificial Intelligence,2013,26(1):603-608.
[11] Alper U,Alper M.A discrete particle swarm optimization method for feature selection in binary classified problems[J].European Journal of Operational Research,2010,206(3):528-539.
[12] Jin C,Jin S W,Qin L N.Attribute selection method based on a hybrid BPNN and PSO algorithms[J].Applied Soft Computing,2012,12(8):2147-2155.
[13] 李中凯，谭建荣，冯毅雄等.基于拥挤距离排序的多目标粒子群优化算法及应用[J].计算机集成制造系统,2008,14(7):1329-1336. Li Zhong-kai,Tan Jian-rong,Feng Yi-xiong,et al.Multi-objective particle swarm optimization based on crowding distance sorting and its application[J].Computer Integrated Manufacturing Systems,2008,14(7):1329-1336.(in Chinese)
[14] Deb K,Pratap A,Agarwal S,et al.A fast and elitist multiobjective genetic algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[15] Tripathi P K,Bandyopadhyay S,Pal S K.Multi-objective particle swarm optimization with time variant inertia and acceleration coefficients[J].Information Sciences,2007,177(22):5033-5049.
[16] Coello C C A,Pulido G T,Lechuga M S.Handing multiple objectives with particle swarm optimization[J].IEEE Transactions on Evolutionary Computation,2004,8(3):256-279.
[17] Zhang Y,Gong D W,Ding Z H.A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch[J].Information Sciences,2012,192(1):213-227.
[18] Gong D W,Zhang Y,Zhang J H.Multi-objective Particle swarm optimization based on minimal particle angle[J].Lecture Notes in Computer Science,2005,3644(1):571-580.

基于多目标微粒群优化的异质数据特征选择

Feature Selection of Heterogeneous Data Based on Multi-Objective Particle Swarm Optimization

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