计算机工程与科学2018，Vol.40Issue(5)：787-792,6.DOI:10.3969/j.issn.1007-130X.2018.05.004
基于选择性集成的并行多分类器融合方法
A parallel multi-classifier fusion approach based on selective ensemble
摘要
Abstract
In order to solve the problem of large time and low accuracy in the process of multi-classifier fusion,a Parallel Multi-Classifier Fusion Approach based on Selective Ensemble (PMCF-SE) is proposed by adopting both the improved Baggingmethod and MapReduce technique.Our approach is based on the MapReduce parallel computing framework.In the Map phase,the base classifiers with better classification performance are selected.In the Reduce phase,the base classifiers of greater diversity are selected,and then the selected base classifiers are fused with the D-S evidence theory.Experimental results show that,compared with the stand-alone environment,the execution efficiency of the classification model in the cluster environment is improved.PMCF-SE has higher classification accuracy than the Bagging algorithm under different numbers of base classifiers.关键词
多分类器融合/选择性集成/D-S证据理论/MapReduce/并行化Key words
multi-classifier fusion/selective ensemble/D-S evidence theory/MapReduce/parallelization分类
信息技术与安全科学引用本文复制引用
陶晓玲,亢蕊楠,刘丽燕..基于选择性集成的并行多分类器融合方法[J].计算机工程与科学,2018,40(5):787-792,6.基金项目
国家自然科学基金(61363006) （61363006）
广西自然科学基金(2016GXNSFAA380098) （2016GXNSFAA380098）
广西云计算与大数据协同创新中心开放课题(YD16803) （YD16803）
桂林电子科技大学研究生科研创新项目(2016YJCX94) （2016YJCX94）
