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20-04-2021

Recurring Drift Detection and Model Selection-Based Ensemble Classification for Data Streams with Unlabeled Data

Authors: Peipei Li, Man Wu, Junhong He, Xuegang Hu

Published in: New Generation Computing | Issue 2/2021

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Abstract

Data stream classification is widely popular in the field of network monitoring, sensor network and electronic commerce, etc. However, in the real-world applications, recurring concept drifting and label missing in data streams seriously aggravate the difficulty on the classification solutions. And this challenge has received little attention from the research community. Motivated by this, we propose a new ensemble classification approach based on the recurring concept drifting detection and model selection for data streams with unlabeled data. First, we build an ensemble model based on the classifiers and clusters. To improve the classification accuracy, we use the ensemble model to predict each data chunk and partition clusters according to the distribution of predicted class labels. Second, we adopt a new concept drifting detection method based on the divergence of concept distributions between adjoining data chunks to distinguish recurring concept drifts. All historical new concepts will be maintained. Meanwhile, we introduce the time-stamp-based weights for base models in the ensemble model. In the selection of the base model, we consider the time-stamp-based weight and the divergence between concept distributions simultaneously. Finally, extensive experiments conducted on four benchmark data sets show that our approach can quickly adapt to data streams with recurring concept drifts, and improve the classification accuracy compared to several state-of-the-art classification algorithms for data streams with concept drifts and unlabeled data.

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Metadata
Title
Recurring Drift Detection and Model Selection-Based Ensemble Classification for Data Streams with Unlabeled Data
Authors
Peipei Li
Man Wu
Junhong He
Xuegang Hu
Publication date
20-04-2021
Publisher
Ohmsha
Published in
New Generation Computing / Issue 2/2021
Print ISSN: 0288-3635
Electronic ISSN: 1882-7055
DOI
https://doi.org/10.1007/s00354-021-00126-2

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