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  • Open Access


    A Novel Semi-Supervised Multi-Label Twin Support Vector Machine

    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

    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 all labels of all samples… More >

  • Open Access


    Multi-Label Chinese Comments Categorization: Comparison of Multi-Label Learning Algorithms

    Jiahui He1, Chaozhi Wang1, Hongyu Wu1, Leiming Yan1,*, Christian Lu2

    Journal of New Media, Vol.1, No.2, pp. 51-61, 2019, DOI:10.32604/jnm.2019.06238

    Abstract Multi-label text categorization refers to the problem of categorizing text through a multi-label learning algorithm. Text classification for Asian languages such as Chinese is different from work for other languages such as English which use spaces to separate words. Before classifying text, it is necessary to perform a word segmentation operation to convert a continuous language into a list of separate words and then convert it into a vector of a certain dimension. Generally, multi-label learning algorithms can be divided into two categories, problem transformation methods and adapted algorithms. This work will use customer's comments about some hotels as a… More >

  • Open Access


    Multi-Label Learning Based on Transfer Learning and Label Correlation

    Kehua Yang1,*, Chaowei She1, Wei Zhang1, Jiqing Yao2, Shaosong Long1

    CMC-Computers, Materials & Continua, Vol.61, No.1, pp. 155-169, 2019, DOI:10.32604/cmc.2019.05901

    Abstract In recent years, multi-label learning has received a lot of attention. However, most of the existing methods only consider global label correlation or local label correlation. In fact, on the one hand, both global and local label correlations can appear in real-world situation at same time. On the other hand, we should not be limited to pairwise labels while ignoring the high-order label correlation. In this paper, we propose a novel and effective method called GLLCBN for multi-label learning. Firstly, we obtain the global label correlation by exploiting label semantic similarity. Then, we analyze the pairwise labels in the label… More >

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