Concept evolution detection based on noise reduction soft boundary

Guo, Husheng; Xia, Haosen; Li, Hai; Wang, Wenjian

doi:10.1016/j.ins.2023.01.115

Cited by 5 publications

(1 citation statement)

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“…The active acceleration strategy is divided into two steps: change detection and model construction. Change detection is performed by examining data distribution from the input, mainly through error analysis [7][8], data distribution, and multiple hypothesis testing. Typical methods in drift detection based on error analysis include the following: drift detection based on sample error rate [9][10] and sample variance [11][12].…”

Section: Related Workmentioning

confidence: 99%

Adaptive Hybrid Ensemble Method for Accelerate Adaptation of Concept Drift

Wang

Guo

Zhang

et al. 2023

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Because the insufficient new distribution training samples after concept drift occurs in streaming data, the performance of online learning model degrades and cannot quickly recover. Therefore, an adaptive hybrid ensemble method for accelerate adaptation of concept drift(AHE\_A$^2$CD) is proposed. After concept drift occurs, the proposed method extracts local information from the streaming data through the weighted base classifiers located in the classifier pool. The local information is supplemented into the current data block through expanding the data to make up for the lack of current distribution data after concept drift occurs and to build an efficient local base learner that conforms to the current data distribution. On this basis, the key data information at different stages is extracted by local base learner, and the current data is adaptively selected by the data distribution to construct diverse global base learner. Through the hybrid ensemble of the high-performance local base learner and the diverse global base learner, this method can adaptively learn the changing streaming data and improve the adaptability after concept drift occurs. Experimental results show that this method can accelerate the convergence of the online learning model after concept drift occurs and improve the real-time performance of streaming data classification.

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