Abstract
This paper proposes an improved multi-label text classification model based on label-correlation called LC-ASVM and it confirms frequent label pairs by the degree of support among feature labels. The model also calculates Spearman rank correlation coefficient of label-pairs to build the label correlation matrix and then measures confidence of SVM matching each category by calculating the projection distance of one point to the hyperplane. Finally the proposed model updates the label correlation matrix through the iterations layer by layer of Adaboost.
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Li, Z., Wang, S., Guo, H. (2017). A Label-Correlated Multi-label Classification Algorithm Based on Spearman Rank Correlation Coefficient. In: Xhafa, F., Patnaik, S., Yu, Z. (eds) Recent Developments in Intelligent Systems and Interactive Applications. IISA 2016. Advances in Intelligent Systems and Computing, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-319-49568-2_8
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DOI: https://doi.org/10.1007/978-3-319-49568-2_8
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