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Ensemble pruning of ELM via migratory binary glowworm swarm optimization and margin distance minimization

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Abstract

Ensemble pruning aims at attaining an ensemble composed of less size of leaners for improving classification ability. Extreme Learning Machine (ELM) is employed as a base learner in this work, in light of its salient features, an initial pool is constructed using ELM. An ensemble composed of ELMs with better performance and diversity can make it perform the best, but the average accuracy of the whole ELMs must be decreased as the increase of diversity among them. Hence there exists a balance between the diversity and the precision of ELMs. Existing works find it via diversity measures or heuristic algorithms, which cannot find the exact tradeoff. To solve the issue, ensemble pruning of ELM via migratory binary glowworm swarm optimization and margin distance minimization (EPEMBM) is proposed utilizing the integration of the proposed migratory binary glowworm swarm optimization (MBGSO) and margin distance minimization (MDM). First, the created ELMs in a pool can be pre-pruned by MDM, and it can markedly downsize the ELMs in the pool, and significantly alleviates its computation overhead. Second, the retaining ELMs are further pruned utilizing MBGSO, and the final ensemble is attained with a high efficiency. Experimental results on 21 UCI classification tasks indicate that EPEMBM outperforms techniques, and that its effectiveness and efficiency. It is a very useful tool for solving the selection problem of ELMs.

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Acknowledgements

This work was supported by the Anhui Provincial Natural Science Foundation under Grant No. 1908085QG298, the National Nature Science Foundation of China under Grant No. 91546108, the Fundamental Research Funds for the Central Universities Nos. JZ2019HGTA0053, JZ2019HGBZ0128, the Anhui Provincial Science and Technology Major Projects No. 201903a05020020, and the Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-making (Hefei University of Technology), Ministry of Education. In addition, we thank Pingfan Xia for the help in some experiments.

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, 230009, China

    Xuhui Zhu, Zhiwei Ni, Liping Ni, Feifei Jin & Zhangjun Wu

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, 230009, China

    Xuhui Zhu, Zhiwei Ni, Liping Ni, Feifei Jin & Zhangjun Wu

  3. Business School, Huzhou University, Huzhou, 313000, China

    Meiying Cheng

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  1. Xuhui Zhu
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Correspondence to Zhiwei Ni.

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Zhu, X., Ni, Z., Ni, L. et al. Ensemble pruning of ELM via migratory binary glowworm swarm optimization and margin distance minimization. Neural Process Lett 52, 2043–2067 (2020). https://doi.org/10.1007/s11063-020-10336-2

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