Journal of Integrative Natural Science (통합자연과학논문집)

The Basic Science Institute Chosun University (조선대학교 기초과학연구원)
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Concept Drift Based on CNN Probability Vector in Data Stream Environment

  • Kim, Tae Yeun (National Program of Excellence in Software center, Chosun University) ;
  • Bae, Sang Hyun (Department of Computer Science & Statistics, Chosun University)
  • Received : 2020.10.02
  • Accepted : 2020.11.28
  • Published : 2020.12.31
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Abstract

In this paper, we propose a method to detect concept drift by applying Convolutional Neural Network (CNN) in a data stream environment. Since the conventional method compares only the final output value of the CNN and detects it as a concept drift if there is a difference, there is a problem in that the actual input value of the data stream reacts sensitively even if there is no significant difference and is incorrectly detected as a concept drift. Therefore, in this paper, in order to reduce such errors, not only the output value of CNN but also the probability vector are used. First, the data entered into the data stream is patterned to learn from the neural network model, and the difference between the output value and probability vector of the current data and the historical data of these learned neural network models is compared to detect the concept drift. The proposed method confirmed that only CNN output values could be used to reduce detection errors compared to how concept drift were detected.

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References

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