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Data stream classification: a review

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Abstract

The tremendous amount of data is generated regularly through areas like networking, telecommunication, stock market, satellite, weather forecasting, etc. So, the classification process becomes important to extract knowledge from such a huge amount of data. The handling of concept drifting data stream and skewed data classification are the major issues and challenges in the data streams mining field. In the presence of concept drift, the performance of the learning algorithm always degrades. On the other side in skewed data problems, majority class accuracy always dominates minority class accuracy which generates the wrong result. This paper discusses the implemented methods which worked for skewed data and concept drifting data as well as their merits and demerits. This paper focused on metrics used in the classification process and issues that arise while learning with skewed and concept drifting data.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Jhulelal Institute of Technology, Nagpur, MS, India

    Kapil K. Wankhade

  2. Department of Computer Science and Engineering, G H Raisoni College of Engineering, Nagpur, MS, India

    Snehlata S. Dongre

  3. Department of Electronics and Telecommunication Engineering, M. G. M.’s College of Engineering, Nanded, MS, India

    Kalpana C. Jondhale

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  1. Kapil K. Wankhade
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Wankhade, K.K., Dongre, S.S. & Jondhale, K.C. Data stream classification: a review. Iran J Comput Sci 3, 239–260 (2020). https://doi.org/10.1007/s42044-020-00061-3

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