融合分类器可信度的数据流集成分类

doi:10.3969/j.issn.0255-8297.2017.02.009

Journal of Applied Sciences ›› 2017, Vol. 35 ›› Issue (2): 226-232.doi: 10.3969/j.issn.0255-8297.2017.02.009

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Data Stream Ensemble Classification Based on Classifier Confidence

LIU San-min¹, LIU Tao¹, WANG Zhong-qun¹, XIU Yu¹, LIU Yu-xia², MENG Chao³

1. College of Computer and Information, Anhui Polytechnic University, Wuhu 241000, Anhui Province, China;
2. Modern Education Technology Center, Anhui Polytechnic University, Wuhu 241000, Anhui Province, China;
3. College of the Internet of Things, Nanjing University of Posts and Communications, Nanjing 210003, China

Received:2016-05-16 Revised:2016-09-20 Online:2017-03-30 Published:2017-03-30

Abstract

Abstract:

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

CLC Number:

TP311

LIU San-min, LIU Tao, WANG Zhong-qun, XIU Yu, LIU Yu-xia, MENG Chao. Data Stream Ensemble Classification Based on Classifier Confidence[J]. Journal of Applied Sciences, 2017, 35(2): 226-232.

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Data Stream Ensemble Classification Based on Classifier Confidence

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