• Machine Learning & Data Mining •

An adaptive ensemble classification method based on deep attribute weighting for data stream

Yao LI(),Zhihai WANG*(),Yan′ge SUN,Wei ZHANG

1. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
• Received:2018-05-25 Online:2018-12-20 Published:2018-12-26
• Contact: Zhihai WANG E-mail:16120396@bjtu.edu.cn;zhhwang@bjtu.edu.cn
• Supported by:
北京市自然科学基金(4182052);国家自然科学基金(61672086);国家自然科学基金(61702030);国家自然科学基金(61771058)

Abstract:

Due to most of the existing data stream ensemble classification algorithms without considering the importance of historical data in the evaluation of the base classifier, while ignoring the treatment of interference with irrelevant attributes and noise attributes, an adaptive ensemble classification method based on deep attribute weighting for data stream (EMDAW) was proposed to effectively combine multiple naive Bayesian models based on depth attribute weighting. In different data blocks, the contribution of different attribute values to the attribution of class attributes was deeply analyzed, and the learned local attribute weights to different attribute values were applied to reduce noise data interference. In the evaluation of the base classifier, the importance of the historical data and the current latest data was weighed. The sub-classifier combination was used to improve the overall classification performance by using the combined voting strategy based on the test case classifier confidence and classification correct rate. By comparing experiments with classical algorithms on multiple benchmark datasets, the proposed algorithm had certain advantages in classification correct rate and concept drift adaptability.

CLC Number:

• TP391
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