文档介绍：: .
计算机科学 R-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，具有更好
的泛化性能。
关键词：SVM 算法； KF 策略；IW_BNAW 算法；MapReduce 框架；TFB 策略
中图法分类号 TP338；TP181 DOI：
Parallel Support Vector Machine Algorithm Based on Clustering and
WOA
LIU Wei-ming，AN Ran and MAO Yi-min
School of Information Engineering，Jiangxi University of Science and Technology，Ganzhou, Jiangxi 341000，
China
Abstract Aiming at the problems of parallel support vector machine(SVM) being sensitive to redundant data,
poor parameter optimization ability and load imbalance in the 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 co