A Developed Feature Selection Method for Classification Based on United Information Gain

被引：0

作者：

Niu, Kun ^{[1
]}

Jiao, Haizhen ^{[1
]}

Gao, Zhipeng ^{[2
]}

Jia, Guannan ^{[1
]}

Yang, Guangyu ^{[1
]}

Cheng, Cheng ^{[3
]}

机构：

[1] Beijing Univ Posts & Telecommun, Sch Software Engn, 10 Xitucheng Rd, Beijing 100876, Peoples R China

[2] Beijing Univ Posts & Telecommun, Inst Network Technol, 10 Xitucheng Rd, Beijing 100876, Peoples R China

[3] Beijing Univ Posts & Telecommun, Sch Informat & Commun Engn, 10 Xitucheng Rd, Beijing 100876, Peoples R China

来源：

2017 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTED, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI) | 2017年

基金：

中国国家自然科学基金;

关键词：

Classification; Feature Selection; United Information Gain; MUTUAL INFORMATION;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In big data times, effective feature selection methods become more and more important because of high dimensional character. In this paper, we propose a developed feature selection method UIG, what effectively improves the accuracy of classifiers. Firstly, all the attributes are systemized pairwise to calculate their united information gain to form a United Information Gain Matrix. Secondly, the United Information Gain Matrix is discretized by a predefined parameter. Finally, UIG searches the biggest full-1 sub-matrix to find the maximum valid subset of attributes. Then classifier can be built by the selected attributes. Experiments on several public data sets show that the proposed method is effective and robust for different situations.

引用

页数：4

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