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A Classifier Chain Algorithm with K-means for Multi-label Classification on Clouds

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Abstract

It has become a basic precursor and facilitator to analyze the emergence of big data with the rise of cloud computing and cloud storage by means of the novel standardized technologies. Then, binary relevance method is carried out as one of the widely known classifier chain methods for multi-label classification. It achieves a higher predictive performance, but it still retains a complex process and takes much computation time. So, in this paper, we present a enhanced classifier chain algorithm with K-means cluster method to confirm the order of the binary classifiers. It has a different strategy that several times of K-means algorithms are employed to get the correlations between labels and to confirm the order of binary classifiers. The algorithm ensures the precise correlations to be transmitted persistently to improve the earlier predictions accuracy. The experiments on a sample data sets of Reuters-21578 show that the approach is effective and appealing in the common cases, it is accurate for a preliminary classification to provide a basis for the further refined classifications.

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Authors and Affiliations

  1. School of Information Science and Engineering, Southeast University, 210096, Nanjing, China

    Zhilou Yu

  2. Technology Center, Inspur Inc., 250101, Jinan, China

    Hong Hao

  3. Department of Computer Science and Technology, Shandong University, 250101, Jinan, China

    Weipin Zhang & Hongjun Dai

Authors
  1. Zhilou Yu
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  2. Hong Hao
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  3. Weipin Zhang
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  4. Hongjun Dai
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Correspondence to Zhilou Yu.

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Yu, Z., Hao, H., Zhang, W. et al. A Classifier Chain Algorithm with K-means for Multi-label Classification on Clouds. J Sign Process Syst 86, 337–346 (2017). https://doi.org/10.1007/s11265-016-1137-2

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  • DOI: https://doi.org/10.1007/s11265-016-1137-2

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