基于聚类和WOA的并行支持向量机算法

doi:10.11896/jsjkx.210500040

计算机科学 ›› 2022, Vol. 49 ›› Issue (7): 64-72.doi: 10.11896/jsjkx.210500040

• 数据库&大数据&数据科学^* • 上一篇下一篇

基于聚类和WOA的并行支持向量机算法

刘卫明, 安冉, 毛伊敏

江西理工大学信息工程学院江西赣州341000

收稿日期:2021-04-30 修回日期:2021-05-17 出版日期:2022-07-15 发布日期:2022-07-12
通讯作者: 毛伊敏(mymlyc@163.com)
作者简介:(m9178mar_45@126.com)
基金资助:
国家自然科学基金(41562019);国家重点研发计划(2018YFC1504705);江西省教育厅科技项目(GJJ151528,GJJ151531)

Parallel Support Vector Machine Algorithm Based on Clustering and WOA

LIU Wei-ming, AN Ran, MAO Yi-min

School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou,Jiangxi 341000,China

Received:2021-04-30 Revised:2021-05-17 Online:2022-07-15 Published:2022-07-12
About author:LIU Wei-ming,born in 1964,professor,master supervisor.His main research interests include data mining,big data and so on.
MAO Yi-min,born in 1970,Ph.D,professor,master supervisor.Her main research interests include data mining,big data and so on.
Supported by:
National Natural Science Foundation of China(41562019),National Key Research and Development of China(2018YFC1504705) and Science and Technology Foundation of Jiangxi Province(GJJ151528,GJJ151531).

摘要/Abstract

摘要： 针对并行SVM在大数据环境下对冗余数据敏感、参数寻优能力差以及并行过程中出现的负载不均衡等问题,提出了一种基于聚类算法和鲸鱼优化算法的并行支持向量机算法MR-KWSVM。首先,该算法提出KF策略来删减冗余数据,利用删减冗余数据后的数据集训练SVM,降低SVM对冗余数据的敏感性;其次,提出了基于非线性收敛因子和自适应惯性权重的鲸鱼智能优化算法IW-BNAW,利用“IW-BNAW”算法获取SVM的最优参数,提高支持向量机的参数寻优能力;最后,在利用MapReduce构造并行SVM的过程中,提出时间反馈策略用于reduce节点的负载调度,提高了集群的并行效率,实现了高并行的SVM。实验结果表明,所提算法不仅保证了SVM在大数据环境下的高并行计算能力,SVM的分类准确度也有明显提高,并且具有更好的泛化性能。

关键词: IW_BNAW算法, KF策略, MapReduce框架, SVM算法, TFB策略

Abstract: Aiming at the problems of parallel support vector machine(SVM) being sensitive to redundant data,poor parameter optimization ability and load imbalance in parallel process in the big data environment,a parallel support vector machine algorithm—MR-KWSVM,based on clustering algorithm and whale optimization algorithm,is proposed.Firstly,the algorithm proposes K-means and fisher(KF) strategy to delete redundant data,and trains SVM with the data set after the redundant data is deleted,which effectively reduces the sensitivity of SVM to redundant data.Secondly,the improved whale optimization algorithm based on nonlinear convergence factor and self-adaptive inertia weight(IW-BNAW) is proposed,and the IW-BNAW algorithm is used to obtain the SVM optimal parameters and improve the parameter optimization ability of the support vector machine.Finally,in the process of constructing parallel SVM with MapReduce,a time feedback strategy(TFB) is proposed for load scheduling of reduce nodes,which improves the parallel efficiency of the cluster and achieves high parallel SVM.Experiment results show that the proposed algorithm not only guarantees the high parallel computing power of SVM in big data environment,but also significantly improves the classification accuracy of SVM,and it has better generalization performance.

Key words: IW-BNAW algorithm, KF strategy, MapReduce frame, SVM algorithm, TFB strategy

中图分类号:

TP338

刘卫明, 安冉, 毛伊敏. 基于聚类和WOA的并行支持向量机算法[J]. 计算机科学, 2022, 49(7): 64-72. https://doi.org/10.11896/jsjkx.210500040

LIU Wei-ming, AN Ran, MAO Yi-min. Parallel Support Vector Machine Algorithm Based on Clustering and WOA[J]. Computer Science, 2022, 49(7): 64-72. https://doi.org/10.11896/jsjkx.210500040

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基于聚类和WOA的并行支持向量机算法

Parallel Support Vector Machine Algorithm Based on Clustering and WOA

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