Abstract
An improved multi-label classification algorithm based on ELM-RBF (Extreme Learning Machine for RBF Networks) is proposed in this article. On the one hand, different clustering analyses are applied to improve the stability of ELM-RBF model; on the other hand, a finite perception method is presented so as to keep the main feature, randomness, of ELM models in a reasonable degree. The main advantage of this F-ELM-RBF (ELM-RBF with Finite Perception) model is an adaptive ability to determine label thresholds which can distinguish the relevant labels from irrelevant labels more scientifically. Statistical experiments show that this algorithm has a good performance on different data sets.
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Chen, P., Li, Q., Zhou, Z., Lu, Z., Zhou, H., Cui, J. (2020). Modified ELM-RBF with Finite Perception for Multi-label Classification. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_46
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DOI: https://doi.org/10.1007/978-981-32-9682-4_46
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