A New Streaming Learning for Stream Chunk Data Classification Based on Incremental Learning and Adaptive Boosting Algorithm

  • Niphat Claypo
  • Anantaporn Hanskunatai
  • Saichon Jaiyen
Conference paper
First Online:
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 566)

Abstract

Currently, stream data classification is a challenge task to discover new useful knowledge from massive and dynamic data in big data era. This paper proposes a streaming learning method based on the incremental learning using a new adaptive boosting algorithm for stream data. The proposed adaptive boosting consists of a new method for updating distribution weight and the new weight voting. This learning method concentrates on learning from sequential chunks of data stream. The distribution weight updating method uses error of previous hypothesis to update the weight. The learning method uses only one data chunk to create a new hypothesis at a time and after learning, the learned data chunk can be thrown away and can learn the new data chunk without using the previous learned data. The experimental results show that the accuracy of the proposed method is higher than other methods in all datasets.

Keywords

Incremental learning Adaptive boosting Stream data Classification 
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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Niphat Claypo
    • 1
  • Anantaporn Hanskunatai
    • 1
  • Saichon Jaiyen
    • 1
  1. 1.Department of Computer ScienceKing Mongkut’s Institute of Technology LadkrabangBangkokThailand

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