Abstract
As one of the central topic in the field of machine learning, multi-label learning gets widely applied in real life. The classical algorithm does not consider the relation of rank and weight between labels simultaneously, while correlation between labels own a certain impact on the quality of classification models, which makes the algorithm unable to be applied in some scenarios and the accuracy of the model is affected. To solve this problem, a new algorithm named weighted multi-label learning with rank preservation (abbrev. WMR) is proposed. WMR extends and optimizes the SVM-based multi-label learning algorithm by introducing two kinds of label pairs, which is called “related-unrelated” and “related-related” label pairs, to measure the rank and weight between labels. The experiment is based on the real datasets and compared to the RankSVM algorithm, and the experimental results show that WMR mines the correlation between labels fully and improve the quality of the classification model effectively.
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This work is supported by the National Fund Major Project (17ZDA166), the Central University Basic Research Business Expenses Special Fund Project (CZY18015).
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Sun, C., Zhou, W., Song, Z., Yin, F., Zhang, L., Bi, J. (2019). Weighted Multi-label Learning with Rank Preservation. In: Jin, H., Lin, X., Cheng, X., Shi, X., Xiao, N., Huang, Y. (eds) Big Data. BigData 2019. Communications in Computer and Information Science, vol 1120. Springer, Singapore. https://doi.org/10.1007/978-981-15-1899-7_22
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DOI: https://doi.org/10.1007/978-981-15-1899-7_22
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