Sample and rule centric approach for associative classification on imbalanced data

被引：0
|
作者：
Yang G. [1 ]
Cui X. [1 ]
Zhang X. [1 ]
机构：
[1] Institute of Systems Engineering, Dalian University of Technology, Dalian
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2017年 / 37卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Associative classification; Imbalanced data; Rule centric processing; Sample centric processing;
D O I：
10.12011/1000-6788(2017)04-1035-11
中图分类号：
学科分类号：
摘要：
The emergency of imbalanced data has brought a great challenge for AC method. To improve AC's performance on imbalanced data, this paper presents key value sampling (KVS) method and rule validation (RV) method respectively from data and rule processing. KVS samples the original imbalanced data and achieves class balance by removing the instances weakly correlated with majority class and increasing those strongly correlated with minority class, which can prevent a lot of useful information from losing and highlight the useful information related with minority class. RV method is to validate the initially generated classifier and improve the rules with bad performances, which can enhance the whole classifier's performance. Through experiment analysis, the methods in this paper can improve the performance of AC on imbalanced data classification. © 2017, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
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收藏
页码：1035 / 1045
页数：10
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