基于粒度和信息熵的并行支持向量机算法

针对大数据环境下并行支持向量机(support vector machine,SVM)算法存在噪声数据较敏感、训练样本数据冗余等问题,提出基于粒度和信息熵的GIESVM-MR(the SVM algorithm by using granularity and information entropy based on MapReduce)算法.该算法首先提出噪声清除策略(noise cleaning,NC)对每个特征属性的重要程度进行评价,获得样本与类别之间的相关度,以达到识别和删除噪声数据的目的;其次提出基于粒度的数据压缩策略(data compression based on granulation,GDC),通过筛选信息粒的方式保留类边界样本删除非支持向量,得到规模较小的数据集,从而解决了大数据环境下训练样本数据冗余问题;最后结合Bagging的思想和MapReduce计算模型并行化训练SVM,生成最终的分类模型.实验表明,GIESVM-MR算法的分类效果更佳,且在大规模的数据集下算法的执行效率更高.

The Parallel SVM Algorithm by Using Granularity and Information Entropy

Aiming at the problems of noise data sensitive and training sample redundancy of parallel SVM algorithm in big data environment, this paper have proposed a parallel SVM algorithm by using granularity and information entropy, named GIESVM-MR. Firstly, the algorithm proposed the NC (noise cleaning) method to evaluate the importance of each feature attribute and obtain the correlation between the sample and the category, which effectively identify and delete noise data. Secondly, a GDC (Data Compression based on Granulation) strategy is proposed, which screen the information granules to retain class boundary samples and delete non-support vectors. Then result in a smaller data set, and solve the problem of training sample data redundancy in a big data environment. Finally, the final classification model is generated by combining the idea of Bagging and MapReduce computing model. Experimental results show that the GIESVM-MR algorithm not only effectively improves the classification accuracy, but also reduces the time complexity of parallel SVM algorithm in big data environment.