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Semi-supervised Classification of Concept Drift Data Stream Based on Local Component Replacement

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Artificial Intelligence (ICAI 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 888))

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Abstract

Being compared with traditional data mining, data stream has three distinct characteristics which pose new challenges to machine learning and data mining. These challenges will become more serious when only few instances are labeled in data stream. In the paper, based on the algorithm of SPASC, a strategy of local component replacement for updating classifier pool is proposed. The proposed strategy defines a vector based on local accuracy to evaluate the adaptability of each “component” of a cluster-based classifier to a new chunk and makes the trained cluster-based classifiers in the pool adapt to the current concept better and faster while retaining as much learned knowledge as possible. The proposed algorithm is compared with the state of the art baseline methods on multiple datasets, the experimental results illustrate the effectiveness of the proposed algorithm.

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Acknowledgment

This work was partially supported by the National Natural Science Foundation of China (61363029, 61662014, 61763007), Guangxi Key Laboratory of Trusted Software (KX201721), Collaborative innovation center of cloud computing and big data (YD16E12), Image intelligent processing project of Key Laboratory Fund (GIIP201505).

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Authors and Affiliations

  1. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Yimin Wen

  2. School of Computer Science and Information Safety, Guilin University of Electronic Technology, Guilin, 541004, China

    Keke Qin & Yimin Wen

Authors
  1. Keke Qin
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  2. Yimin Wen
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Corresponding author

Correspondence to Yimin Wen .

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Editors and Affiliations

  1. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  2. Hong Kong University of Science and Technology, Hong Kong SAR, China

    Qiang Yang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. JD Finance, Beijing, China

    Yu Zheng

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Qin, K., Wen, Y. (2018). Semi-supervised Classification of Concept Drift Data Stream Based on Local Component Replacement. In: Zhou, ZH., Yang, Q., Gao, Y., Zheng, Y. (eds) Artificial Intelligence. ICAI 2018. Communications in Computer and Information Science, vol 888. Springer, Singapore. https://doi.org/10.1007/978-981-13-2122-1_8

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  • DOI: https://doi.org/10.1007/978-981-13-2122-1_8

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-2121-4

  • Online ISBN: 978-981-13-2122-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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