Three-way Decisions Based Bayesian Network

被引：0

作者：

Gu, Yannan ^{[1
]}

Jia, Xiuyi ^{[1
]}

Shang, Lin ^{[2
]}

机构：

[1] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing, Jiangsu, Peoples R China

[2] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing, Jiangsu, Peoples R China

来源：

PROCEEDINGS OF 2015 IEEE INTERNATIONAL CONFERENCE ON PROGRESS IN INFORMATCS AND COMPUTING (IEEE PIC) | 2015年

基金：

中国国家自然科学基金;

关键词：

rough set; Bayesian Network; three-way decisions; cost-sensitive classification;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Rough set theory provides a ternary classification method by approximating a set into positive, boundary and negative regions. A Bayesian Network classifier is a directed acyclic graph model that encodes a joint probability distribution over a set of random variables. In this paper, we propose a probabilistic rough set model, three-way decisions based Bayesian Network (3DBN), to integrate these two classification techniques. Several comparison experiments are implemented to evaluate the performance of 3DBN. Experimental results show that the proposed method can get a better performance on accuracy and misclassification cost.

引用

页码：51 / 55

页数：5

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