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Adaptive Decision Threshold-Based Extreme Learning Machine for Classifying Imbalanced Multi-label Data

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Abstract

Multi-label learning is a popular area of machine learning research as it is widely applicable to many real-world scenarios. In comparison with traditional binary and multi-classification tasks, the multi-label data are more easily impacted or destroyed by an imbalanced data distribution. This paper describes an adaptive decision threshold-based extreme learning machine algorithm (ADT-ELM) that addresses the imbalanced multi-label data classification problem. Specifically, the macro and micro F-measure metrics are adopted as the optimization functions for ADT-ELM, and the particle swarm optimization algorithm is employed to determine the optimal decision threshold combination. We use the optimized thresholds to make decision for future multi-label instances. Twelve baseline multi-label data sets are used in a series of experiments o verify the effectiveness and superiority of the proposed algorithm. The experimental results indicate that the proposed ADT-ELM algorithm is significantly superior to many state-of-the-art multi-label imbalance learning algorithms, and it generally requires less training time than more sophisticated algorithms.

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Acknowledgements

This work was supported by Natural Science Foundation of Jiangsu Province of China under Grant No. BK20191457, Open Project of Artificial Intelligence Key Laboratory of Sichuan Province under Grant No. 2019RYJ02, National Natural Science Foundation of China under Grant Nos. 61305058 and 61572242, China Postdoctoral Science Foundation under Grant Nos. 2013M540404 and 2015T80481.

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Authors and Affiliations

  1. School of Computer, Jiangsu University of Science and Technology, No. 2, Mengxi Road, Zhenjiang, 212003, Jiangsu, People’s Republic of China

    Shang Gao, Wenlu Dong, Ke Cheng, Xibei Yang, Shang Zheng & Hualong Yu

  2. Artificial Intelligence Key Laboratory of Sichuan Province, Sichuan University of Science and Engineering, Yibin, 644000, People’s Republic of China

    Shang Gao, Shang Zheng & Hualong Yu

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  1. Shang Gao
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  2. Wenlu Dong
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  5. Shang Zheng
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  6. Hualong Yu
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Correspondence to Hualong Yu.

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Gao, S., Dong, W., Cheng, K. et al. Adaptive Decision Threshold-Based Extreme Learning Machine for Classifying Imbalanced Multi-label Data. Neural Process Lett 52, 2151–2173 (2020). https://doi.org/10.1007/s11063-020-10343-3

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