摘要
本文针对大数据环境下的多标签集成分类器的技术积累、基于Boos Texter算法的迭代次数优化方法、基准子集特征数量分类器算法构造的现状,根据文本分类存在问题进行分析,为满足今后分类器改进提出大数据环境下的隐性语义索引改进和不断改进策略。
In this paper,the present situation of the technology accumulation of multi label integrated classifier in large data environment,the optimization method of iterations based on Boos Texter algorithm and the algorithm of datum subset feature quantity classifier are constructed,and the problems in the text classification are analyzed,and the large data environment is put forward to meet the future classifier improvement.Recessive semantic index improvement and continuous improvement strategy.
作者
李峤
王茹娟
Li Jiao;Wang Rujuan(School of humanities,Northeast Normal University,Changchun Jilin,130117)
出处
《电子测试》
2018年第14期115-116,共2页
Electronic Test
基金
吉林省教育厅"十三五"科学技术研究项目"大数据环境下语义索引技术的研究"(吉教科合字[2016]第159号)
关键词
多标签集成分类器
基准
文本分类
multi label ensemble classifier
benchmark
text categorization
