计算机技术与发展2016，Vol.26Issue(9)：149-153,157,6.DOI:10.3969/j.issn.1673-629X.2016.09.033
基于最小最大模块化集成特征选择的改进
Improvement of Multi-classification Integrated Selection Based on Min-Max-Module
摘要
Abstract
With the expansion of the data size,a single weak classifier has been unable to predict unknown samples accurately. To solve this problem,an integrated learning is proposed. Combined the integrated learning and classification,the idea of integration is also used in the feature selection at the same time. For the increase of sample prediction accuracy,a strategy based on Min-Max-Module (M3) is put forward. It makes integrated learning applied to feature selection algorithms and classifier,and compares four kinds of integration strategies as well as three different classification methods. The results show that the proposed method can be able to achieve good results in most ca-ses,and can well handle imbalanced data sets.关键词
特征选择/集成学习/最小最大模块化策略/不平衡数据Key words
feature selection/integrated learning/Min-Max-Module(M3)/Imbalance Data Sets (IDS)分类
信息技术与安全科学引用本文复制引用
周丰,王未央..基于最小最大模块化集成特征选择的改进[J].计算机技术与发展,2016,26(9):149-153,157,6.基金项目
国家自然科学基金青年项目(61303100) （61303100）
