基于改进斑点鬣狗优化算法的同步优化特征选择

doi:10.11772/j.issn.1001-9081.2020081192

计算机应用 ›› 2021, Vol. 41 ›› Issue (5): 1290-1298.DOI: 10.11772/j.issn.1001-9081.2020081192

所属专题：人工智能

基于改进斑点鬣狗优化算法的同步优化特征选择

贾鹤鸣^1,2, 姜子超², 李瑶², 孙康健²

1. 三明学院信息工程学院, 福建三明 365004;
2. 东北林业大学机电工程学院, 哈尔滨 150040

收稿日期:2020-08-10 修回日期:2020-10-18 出版日期:2021-05-10 发布日期:2020-11-25
通讯作者: 贾鹤鸣
作者简介:贾鹤鸣(1983-),男,辽宁辽阳人,教授,博士,CCF会员,主要研究方向:群体智能优化、特征选择、多阈值图像分割;姜子超(1995-),男,黑龙江齐齐哈尔人,硕士研究生,主要研究方向:机器学习、群智能优化、特征选择;李瑶(1997-),女,黑龙江伊春人,硕士研究生,主要研究方向:群智能优化、特征选择;孙康健(1996-),男,辽宁锦州人,硕士研究生,主要研究方向:群智能优化、特征选择。
基金资助:
教育部产学合作协同育人项目（202002064014）；福建省教育厅中青年教师教育科研项目（JAT200618）；三明市科技计划引导性项目（2020-G-61）；三明学院引进高层次人才科研启动经费支持项目（20YG14）；三明学院科学研究发展基金资助项目（B202009）；三明学院高教研究课题（SHE2013）；福建省农业物联网应用重点实验室开放研究基金资助项目（ZD2101）。

Simultaneous feature selection optimization based on improved spotted hyena optimizer algorithm

JIA Heming^1,2, JIANG Zichao², LI Yao², SUN Kangjian²

1. School of Information Engineering, Sanming University, Sanming Fujian 365004, China;
2. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin Heilongjiang 150040, China

Received:2020-08-10 Revised:2020-10-18 Online:2021-05-10 Published:2020-11-25
Supported by:
This work is partially supported by the Project of Ministry of Education Industry-University Cooperation Collaborative Education (202002064014),the Educational Research Project of Young and Middle-aged Teachers in Fujian Province (JAT200618), the Guiding Science and Technology Project in Sanming City (2020-G-61), the Scientific Research Start Fund for Sanming University Introduced High-Level Talents (20YG14), the Scientific Research and Development Fund of Sanming University (B202009), the Project of Sanming University Higher Education Research (SHE2013), the Open Research Fund of Fujian Provincial Key Laboratory of Agriculture Internet of Things Application (ZD2101)

摘要/Abstract

摘要： 针对传统支持向量机（SVM）在封装式特征选择中分类精度低、特征子集选择冗余以及计算效率差的不足，利用元启发式优化算法同步优化SVM与特征选择。为改善SVM分类效果以及选择特征子集的能力，首先，利用自适应差分进化（DE）算法、混沌初始化与锦标赛选择策略对斑点鬣狗优化（SHO）算法改进，以增强其局部搜索能力并提高其寻优效率与求解精度；其次，将改进后的算法用于特征选择与SVM参数调整的同步优化中；最后，在UCI数据集进行特征选择仿真实验，采取分类准确率、选择特征数、适应度值及运行时间来综合评估所提算法的优化性能。实验结果证明，改进算法的同步优化机制能够在高分类准确率下降低特征选择的数目，该算法比传统算法更适合解决封装式特征选择问题，具有良好的应用价值。

关键词: 斑点鬣狗优化算法, 差分进化, 混沌初始化, 锦标赛选择, 支持向量机, 封装式特征选择

Abstract: Aiming at the disadvantages of traditional Support Vector Machine (SVM) in the wrapper feature selection:low classification accuracy, redundant feature subset selection and poor computational efficiency, the meta-heuristic optimization algorithm was used to simultaneously optimize SVM and feature selection. In order to improve the classification effect of SVM and the ability of feature subset selection, firstly, the Spotted Hyena Optimizer (SHO) algorithm was improved by using the adaptive Differential Evolution (DE) algorithm, chaotic initialization and tournament selection strategy, so as to enhance its local search ability as well as improve its optimization efficiency and solution accuracy; secondly, the improved algorithm was applied to the simultaneous optimization of feature selection and SVM parameter adjustment; finally, a feature selection simulation experiment was carried out on the UCI datasets, and the classification accuracy, the number of selected features, the fitness value and the running time were used to comprehensively evaluate the optimization performance of the proposed algorithm. Experimental results show that the simultaneous optimization mechanism of the improved algorithm can reduce the number of selected features with high classification accuracy, and compared to the traditional algorithms, this algorithm is more suitable for solving the problem of wrapper feature selection, which has good application value.

Key words: Spotted Hyena Optimizer (SHO) algorithm, Differential Evolution (DE), chaotic initialization, tournament selection, Support Vector Machine (SVM), wrapper feature selection

中图分类号:

TP301.6

贾鹤鸣, 姜子超, 李瑶, 孙康健. 基于改进斑点鬣狗优化算法的同步优化特征选择[J]. 计算机应用, 2021, 41(5): 1290-1298.

JIA Heming, JIANG Zichao, LI Yao, SUN Kangjian. Simultaneous feature selection optimization based on improved spotted hyena optimizer algorithm[J]. Journal of Computer Applications, 2021, 41(5): 1290-1298.

参考文献

[1] 何清, 李宁, 罗文娟, 等. 大数据下的机器学习算法综述[J]. 模式识别与人工智能,2014,27(4):327-336.(HE Q,LI N,LUO W J,et al. A survey of machine learning algorithms for big data[J]. Pattern Recognition and Artificial Intelligence,2014,27(4):327-336.)
[2] LI J,CHENG K,WANG S,et al. Feature selection:a data perspective[J]. ACM Computing Surveys,2016,50(6):No. 94.
[3] 李郅琴, 杜建强, 聂斌, 等. 特征选择方法综述[J]. 计算机工程与应用,2019,55(24):10-19.(LI Z Q,DU J Q,NIE B,et al. Summary of feature selection methods[J]. Computer Engineering and Applications,2019,55(24):10-19.)
[4] JIA H,XING Z,SONG W. A new hybrid seagull optimization algorithm for feature selection[J]. IEEE Access,2019,7:49614-49631.
[5] 张翠军, 陈贝贝, 周冲, 等. 基于多目标骨架粒子群优化的特征选择算法[J]. 计算机应用,2018,38(11):3156-3160,3166. (ZHANG C J,CHEN B B,ZHOU C,et al. Feature selection algorithm based on multi-objective bare-bones particle swarm optimization[J]. Journal of Computer Applications, 2018, 38(11):3156-3160,3166.)
[6] MAFARJA M M,MIRJALILI S. Whale optimization approaches for wrapper feature selection[J]. Applied Soft Computing,2018,62:441-453.
[7] 张霞, 庞秀平. 基于增强蜂群优化算法的特征选择算法[J]. 计算机应用,2016,36(5):1307-1312,1318.(ZHANG X,PANG X P. Feature selection algorithms base on enhanced bee colony optimization algorithm[J]. Journal of Computer Applications, 2016,36(5):1307-1312,1318.)
[8] CORTES C,VAPNIK V. Support-vector networks[J]. Machine Learning,1995,20(3):273-297.
[9] 庄严, 白振林, 许云峰. 基于蚁群算法的支持向量机参数选择方法研究[J]. 计算机仿真,2011,28(5):216-219.(ZHUANG Y, BAI Z L,XU Y F. Research on parameters of support vector machine based on ant colony algorithm[J]. Computer Simulation, 2011,28(5):216-219.)
[10] HUANG C L,WANG C J. A GA-based feature selection and parameters optimization for support vector machines[J]. Expert Systems with Applications,2006,31(2):231-240.
[11] 张进, 丁胜, 李波. 改进的基于粒子群优化的支持向量机特征选择和参数联合优化算法[J]. 计算机应用,2016,36(5):1330-1335.(ZHANG J,DING S,LI B. Improved particle swarm optimization algorithm for support vector machine feature selection and optimization of parameters[J]. Journal of Computer Applications,2016,36(5):1330-1335.)
[12] STORN R,PRICE K. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. Journal of Global Optimization,1997,11(4):341-359.
[13] ZHENG X,LU D,WANG X,et al. A cooperative coevolutionary biogeography-based optimizer[J]. Applied Intelligence,2015,43(1):95-111.
[14] POLI R, KENNEDY J, BLACKWELL T. Particle swarm optimization[J]. Swarm Intelligence,2007,1(1):33-57.
[15] YANG X S, DEB S. Cuckoo search:recent advances and applications[J]. Neural Computing and Applications,2014,24(1):169-174.
[16] MIRJALILI S,MIRJALILI S M,LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software,2014,69:46-61.
[17] MIRJALILI S,LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software,2016,95:51-67.
[18] ALABERT A,BERTI A,CABALLERO R,et al. No-free-lunch theorems in the continuum[J]. Theoretical Computer Science, 2015,600:98-106.
[19] DHIMAN G,KUMAR V. Spotted hyena optimizer:a novel bioinspired based metaheuristic technique for engineering applications[J]. Advances in Engineering Software,2017,114:48-70.
[20] KUMAR V,KAUR A. Binary spotted hyena optimizer and its application to feature selection[J]. Journal of Ambient Intelligence and Humanized Computing,2020,11(7):2625-2645.
[21] JIA H, LI J, SONG W, et al. Spotted hyena optimization algorithm with simulated annealing for feature selection[J]. IEEE Access,2019,7:71943-71962.
[22] LIN Q,ZHU Q,HUANG P,et al. A novel hybrid multi-objective immune algorithm with adaptive differential evolution[J]. Computers and Operations Research,2015,62:95-111.
[23] 王日宏, 李祥, 李娜. 基于高斯扰动和混沌初始化的狼群算法[J]. 计算机工程与设计,2019,40(10):2879-2884.(WANG R H, LI X, LI N. Wolf pack algorithm based on Gaussian disturbance and chaotic initialization[J]. Computer Engineering and Design,2019,40(10):2879-2884.)
[24] KILIÇ H,YÜZGEÇ U. Improved antlion optimization algorithm via tournament selection and its application to parallel machine scheduling[J]. Computers and Industrial Engineering,2019, 132:166-186.
[25] ELAZIZ M A,XIONG S,JAYASENA K P N,et al. Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution[J]. Knowledge-Based Systems,2019,169:39-52.
[26] ALJARAH I,Al-ZOUBI A M,FARIS H,et al. Simultaneous feature selection and support vector machine optimization using the grasshopper optimization algorithm[J]. Cognitive Computation, 2018,10(3):478-495.
[27] TUBA E,STRUMBERGER I,BEZDAN T,et al. Classification and feature selection method for medical datasets by brain storm optimization algorithm and support vector machine[J]. Procedia Computer Science,2019,162:307-315.
[28] 孙广路, 宋智超, 刘金来, 等. 基于最大信息系数和近似马尔科夫毯的特征选择方法[J]. 自动化学报,2017,43(5):795-805. (SUN G L,SONG Z C,LIU J L,et al. Feature selection method based on maximum information coefficient and approximate Markov blanket[J]. Acta Automatica Sinica,2017,43(5):795-805.)
[29] DUA D,GRAFF C. UCI Machine learning repository[EB/OL].[2019-12-08]. http://archive.ics.uci.edu/ml.

基于改进斑点鬣狗优化算法的同步优化特征选择

Simultaneous feature selection optimization based on improved spotted hyena optimizer algorithm

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