Computer Science ›› 2020, Vol. 47 ›› Issue (2): 37-43.doi: 10.11896/jsjkx.190100092

• Database & Big Data & Data Science • Previous Articles     Next Articles

Support Vector Machine Model Based on Grey Wolf Optimization Fused Asymptotic

WU Yu-kun,XIAO Jie,Wei William LEE,LOU Ji-lin   

  1. (College of Computer Science & Technology,Zhejiang University of Technology,Hangzhou 310023,China)
  • Received:2019-01-12 Online:2020-02-15 Published:2020-03-18
  • About author:WU Yu-kun,born in 1980,doctorial student,is member of China Computer Federation (CCF).His main research interests include machine learning and big data;Wei William LEE,born in 1958,Ph.D,professor.His main research interests include big data,block chain,IOT and smart city development.
  • Supported by:
    This work supported by the National Natural Science Foundation of China (61502422) and Natural Science Foundation of Zhejiang Province (LY18F020028).

Abstract: The development of big data requires higher accuracy of data classification.The wide application of support vector machine (SVM) requires an efficient method to construct an SVM classifier with strong classification ability.The kernel parameter,penalty parameter and feature subsets of dataset have an important impact on the complexity and prediction accuracy of the model.In order to improve the classification performance of SVM,the asymptotic of SVM was integrated into the gray wolf optimization (GWO) algorithm,and a new SVM classifier model was proposed.The model optimizes feature selection and parameter optimization of SVM at the same time.The new grey wolf individual integrating the asymptotic property of SVM directs the search space of grey wolf optimization algorithm to the optimal region in super-parameter space,and can obtain the optimal solution faster.In addition,a new fitness function,which combines the classification accuracy obtained from the method,the number of chosen features and the number of support vectors,was proposed.The new fitness function and GWO fused asymptotic lead the search to the optimal solution.This paper used several classical datasets on UCI to verify the proposed model.Compared with the grid search algorithm,the gray wolf optimization algorithm without asymptotic convergence and other methods in the literature,the classification accuracy of the proposed algorithm has different degrees of improvement on different data sets.The experimental results show that the proposed algorithm can find the optimal parameters and the smallest feature subset of SVM,with higher classification accuracy and less average processing time.

Key words: Gray wolf optimization algorithm, Parameters optimization, Feature selection, Asymptotic, Support vector machines

CLC Number: 

  • TP391
[1]DRUCKER H,WU H,VAPNIK V N.Support vector machines for spam categorization[J].IEEE Transactions on Neural Networks,1999,10(5) 1048-1054.
[2]VATSA M,SINGH R,NOORE A.Improving biometric recognition accuracy and robustness using dwt and and svm watermarking[J].IEICE Electronics Express,2005,2(12):362-367.
[3]BYVATOV E,SCHNEIDER G.Support vector machine applications1in1bioinformatics[J].Applied bioinformatics,2002,2(2):67-77.
[4]DOUCET J P,BARBAULT F,XIA H,et al.Nonlinear svm approaches to qspr/qsar studies and drug design[J].Current Computer-Aided Drug Design,2007,3(4):263-289.
[5]LIN S W,YING K C,CHEN S C,et al.Particle swarm optimization for parameter determination and feature selection of support vector machines[J].Expert Systems with Applications,2008,35(4):1817-1824.
[6]ZHANG X L,CHEN X F,HE Z J.An aco-based algorithm for parameter optimization of support vector11machines[J].Expert Systems with Applications,2010,37(9):6618-6628.
[7]MIRJALILI S,MIRJALILI S M,LEWIS A.Grey wolf optimizer[J].Advances in Engineering Software,2014,69(5):46-61.
[8]JALILIS M R.How effective is the grey wolf optimizer intrai-ning multilayer perceptrons[J].Applied Intelligence,2015,42(4):608-619.
[9]SONG H M,SULAIMAN M H,MOHAMED M R.An application of grey wolf optimizer for solving combined economic emission dispatch problems [J].International Review on Modelling and Simulations,2014,7(5):838-844.
[10]SULAIMAN M H,MUSTAFFA Z,MOHAMED M R,et al. Using the grey wolf optimizer for solving optimal reactive power dispatch problem[J].Applied Soft Computing,2015,32:286-292.
[11]LONG W,ZHAO D Q,XU S J.Improved grey wolf optimization algorithm for constrained optimization problem[J].Journal of Computer Applications,2015,35(9):2590-2595.
[12]FRIEDRICHSF,IGEL C.Evolutionary tuning of multiple SVM parameters[J].Neurocomputing,2005,64(2):107-117.
[13]HUANG C L,WANG C J.A GA-based feature selection and parameters optimization for support vector machines[J].Expert Systems with Applications,2006,31:231-240.
[14]WU C H,TZENG G H,LIN R H.A novel hybrid genetic algorithm for kernel function and parameter optimization in support vector regression[J].Expert Systems with Applications,2009,36(3):4725-4735.
[15]CHEN J Y,XIONG H,ZHENG H B.Parameters Optimization for SVM Based on Particle Swarm Algorithm[J].Computer Science,2018,45(6):197-203.
[16]GUO L,WU Y X,ZHAO L,et al.Classification of Mental Task From EEG Signals Using Immune Feature Weighted Support Vector Machines[J].IEEE Transactions on Magnetics,2011,47 (5):866-869.
[17]THARWAT A.A BA-based algorithm for parameter optimization of Support Vector Machine[J].Pattern Recognition Letters,2017,93:13-22.
[18]ALA′M A,HOSSAM F,JA′FAR A.Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingua contexts[J].Knowledge-based systems,2018,8(153):91-104.
[19]CHAO C F,HORNG M H.The construction of support vector machine classifier using the firefly algorithm[J].Computational Intelligence and Neuroscience,2015,1:1-8.
[20]PRABUKUMAR M,AGILANDEESWARI L,GANESAN K. An intelligent lung cancer diagnosis system using cuckoo search optimization and support vector machine classifier[J].Journal of Ambient Intelligence and Humanized Computing,2019,1(10):267-293.
[21]XUE H X,BAI Y P,HU H P.A Novel Hybrid Model Based on TVIW-PSO-GSA Algorithm and Support Vector Machine for Classification Problems[J].IEEE Access,2019(7):27789-27801.
[22]MAO K Z.Feature subset selection for support vector machines through discriminative function pruning analysis[J].IEEE Transactions on Systems,Man,and Cybernetics,2004,34(1):60-67.
[23]RAYMER M L,PUNCH W F,GOODMAN E D,et al.Dimensionality reduction using genetic algorithms[J].IEEE Transactions on Evolutionary Computation,2000(2):164-171.
[24]YANG J,HONAVAR V.Feature subset selection using a genetic algorithm[J].IEEE Intelligent Systems,1998,13(2):44-49.
[25]GUYON I,WESTON J,BARNHILL S,et al.Gene selection for cancer classi?cation using support vector machines[J].Machine Learning,2002,46(1/2/3):389-422.
[26]LIN S W,YING K C,Chen S C,et al.Particle swarm optimization for parameter determination and feature selection of support vector machines[J].Expert Systems with Applications,2008,35(4):1817-1824.
[27]ZHOU T,LU H L,WANG W W,et al.GA-SVM based feature selection and parameter optimization in hospitalization expense modeling [J].Applied Soft Computing,2019,75 (2):323-332.
[28]FARIS H,HASSONAH M A,AL-ZOUBI,et al.A multi-verse optimizer approach for feature selection and optimizing SVM parameters based on a robust system architecture[J].Neural Computing and Applications,2017,30(8):2355-2369.
[29]ALJARAH I.Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm[J].Cognitive Computation,2018,10(3):478-495.
[30]CHEN Y,MA H W.Feature selection and parameter optimization of support vector machine based on the bees algorithm[J].Modular Machine Tool&Automatic Manufacturing Technique,2013 (11):41-43.
[31]LIN K C,CHEN S Y,JASON C,et al.Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Artificial Fish Swarm Algorithms[J].Mathematical Problems in Engineering,2015,Article ID 604108.
[32]SHEN Y L,SONG J,WAN Z C.Improved Fireworks Algorithm for Support Vector Machine Feature Selection and Parameters Optimization[J].Microelectronics&Computer,2018,35(1):21-25.
[33]IKEDA K,AOISHI T.An asymptotic statistical analysis of support vector machines with soft margins[J].Neural Networks,2005,18(3):251-259.
[34]ZHAO M Y.Feature selection and parameter optimization for support vector machines:A new approach based on genetic algorithm with feature chromosomes[J].Expert Systems with Applications,2011,38(5):5197-5204.
[35]KEERTHI S S,LIN C J.Asymptotic behaviors of support vector machines with Gaussian kernel[J].Neural Computation,2003,15:1667-1689.
[36]CHEN Z.A Parallel Genetic Algorithm Based Feature Selection and Parameter Optimization for Support Vector Machine[J].Scientific Programming,2016:1-10.
[37]CHANG C C,LIN C J.LIBSVM:a library for support vector machines[J].ACM Transactions on Intelligent Systems and Technology,2011,2(3).
[38]PHAN A V,NGUYEN M L,BUI L T.Feature weighting and SVM parameters optimization based on genetic algorithms for classification problems[J].Applied Intelligence,2016,46(2):455-469.
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