收稿日期: 2016-05-16
修回日期: 2016-09-20
网络出版日期: 2017-03-30
基金资助
国家自然科学基金（No.61300170，No.71371012）；安徽省高校优秀人才重点项目基金（No.2013SQRL034ZD）；安徽省自然科学基金（No.1608085MF147）；安徽省教育厅一般项目基金（No.TSKJ2014B10，No.TSKJ2016B05）资助
Data Stream Ensemble Classification Based on Classifier Confidence
Received date: 2016-05-16
Revised date: 2016-09-20
Online published: 2017-03-30
刘三民, 刘涛, 王忠群, 修宇, 刘余霞, 孟超 . 融合分类器可信度的数据流集成分类[J]. 应用科学学报, 2017 , 35(2) : 226 -232 . DOI: 10.3969/j.issn.0255-8297.2017.02.009
A weight computation policy based on confidence is presented to deal with the problem in the sub-classifier's weight in dynamic data stream ensemble classification. The policy fully considers influence of the sample on the weight of the sub-classifier. Uncertainty of the prediction result is described by information entropy, and relationship between the classifier confidence and the samples established. Thus, the computation method of classifier's confidence is defined. According to the requirements of dynamic data stream classification and traits of concept drift, a dynamic weight ensemble model is built by batch learning and time policy. Theoretical analysis and experimental results show feasibility of the presented schema, which is better than traditional methods.
Key words： ensemble learning; confidence; data stream classification; concept drift
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