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MapReduce-based adaptive random forest algorithm for multi-label classification

  • Machine Learning - Applications & Techniques in Cyber Intelligence
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Abstract

Due to the complexity of data characteristics, multi-label learning in data mining has been proposed by scholars to solve the problem of information knowledge in the era of big data. In the era of big data, the complexity of the data structures makes it impossible for traditional single-label learning methods to meet the needs of technological development. Moreover, the importance of multi-label learning is gradually becoming evident. The random forest (RF) algorithm is regarded as one of the best classification algorithms. In this study, the traditional decision tree algorithm was improved, and the traditional RF method was converted into an adaptive RF (ARF) method for multi-label classification. By experiments, the effectiveness of the proposed method was verified. The RF method may not be able to classify massive data in a short time, but Hadoop, which was by Apache, is suitable for data-intensive tasks. On this basis, we modified the MapReduce programming mode to make it suitable for the proposed ARF method. This method was implemented on the cloud platform, and the time effectiveness of the parallel model was verified by experiments.

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Acknowledgements

This paper is supported by Natural Science Foundation of China (No. 61673354), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan), the State Key Lab of Digital Manufacturing Equipment & Technology (DMETKF2018020), and Huazhong University of Science & Technology.

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Authors and Affiliations

  1. Faculty of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, 430205, Hubei, China

    Qinghua Wu & Haihui Wang

  2. School of Computer Science, China University of Geosciences, Wuhan, 430074, Hubei, China

    Xuesong Yan

  3. State Key Lab of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan, 430074, Hubei, China

    Xuesong Yan

  4. School of Automation, China University of Geosciences, Wuhan, 430074, Hubei, China

    Xiaobo Liu

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  1. Qinghua Wu
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  2. Haihui Wang
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  3. Xuesong Yan
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  4. Xiaobo Liu
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Correspondence to Xuesong Yan.

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Wu, Q., Wang, H., Yan, X. et al. MapReduce-based adaptive random forest algorithm for multi-label classification. Neural Comput & Applic 31, 8239–8252 (2019). https://doi.org/10.1007/s00521-018-3900-8

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