Abstract
Extreme Learning Machine (ELM) has drawn more and more attention in the machine learning fields because of its fast training speed and good generalization ability. In this paper, we employ ELM algorithm to deal with multi-label classification problems. The essence of the proposed algorithm is to convert the multi-label classification problem into some single-label classifications, and fully considers the relationship among different labels. The hidden nodes of ELM algorithm for one label classification come from three parts. Some of them are initialized nodes after PCA dimensionality reduction. And we design a backup pool to select appropriate hidden nodes. In addition, the nodes of the previous label classification would flow into the classification of the current label. In the simulation part, three famous databases demonstrate the satisfied classification accuracy of the proposed method.
Keywords
Supported by organization the National Natural Science Foundation of China (No. 61806208, 61502498), Fundamental Research Funds for the Central Universities (No. 3122018S008), Tianjin Education Committee Research Project (No. 2018KJ246).
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Zhang, H., Yang, J., Jia, G., Han, S. (2020). Extreme Learning Machine for Multi-label Classification. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_19
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DOI: https://doi.org/10.1007/978-3-030-23307-5_19
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