Qing Ai1,2,*, Yude Kang1, Anna Wang2
Intelligent Automation & Soft Computing, Vol.27, No.1, pp. 205-220, 2021, DOI:10.32604/iasc.2021.013357
- 07 January 2021
Abstract Multi-label learning is a meaningful supervised learning task in which each sample may belong to multiple labels simultaneously. Due to this characteristic, multi-label learning is more complicated and more difficult than multi-class classification learning. The multi-label twin support vector machine (MLTSVM) [], which is an effective multi-label learning algorithm based on the twin support vector machine (TSVM), has been widely studied because of its good classification performance. To obtain good generalization performance, the MLTSVM often needs a large number of labelled samples. In practical engineering problems, it is very time consuming and difficult to obtain… More >
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