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Ensemble Classification of Data Streams Based on Attribute Reduction and a Sliding Window

National Digital Switching System Engineering & Technological Research and Development Center (NDSC), Zhengzhou 450000, China
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Appl. Sci. 2018, 8(4), 620; https://doi.org/10.3390/app8040620
Received: 25 March 2018 / Revised: 11 April 2018 / Accepted: 12 April 2018 / Published: 16 April 2018
With the current increasing volume and dimensionality of data, traditional data classification algorithms are unable to satisfy the demands of practical classification applications of data streams. To deal with noise and concept drift in data streams, we propose an ensemble classification algorithm based on attribute reduction and a sliding window in this paper. Using mutual information, an approximate attribute reduction algorithm based on rough sets is used to reduce data dimensionality and increase the diversity of reduced results in the algorithm. A double-threshold concept drift detection method and a three-stage sliding window control strategy are introduced to improve the performance of the algorithm when dealing with both noise and concept drift. The classification precision is further improved by updating the base classifiers and their nonlinear weights. Experiments on synthetic datasets and actual datasets demonstrate the performance of the algorithm in terms of classification precision, memory use, and time efficiency. View Full-Text
Keywords: ensemble classification; data stream; concept drift; attribute reduction; sliding window ensemble classification; data stream; concept drift; attribute reduction; sliding window
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Chen, Y.; Li, O.; Sun, Y.; Li, F. Ensemble Classification of Data Streams Based on Attribute Reduction and a Sliding Window. Appl. Sci. 2018, 8, 620.

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