Abstract
Multi-label classification exists widely in medical analysis or image annotation. Although there are some algorithms to train models for multi-label classification, few of them are able to extract comprehensible rules. In this paper, we propose a multi-label decision tree algorithm based on fuzzy rough sets, named ML-FRDT. This method can tackle with symbolic, continuous and fuzzy data. We conduct experiments on two multi-label datasets. And the experiment results show that ML-FRDT achieves good performance than some well-established multi-label classification algorithms.
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Wang, X., An, S., Shi, H., Hu, Q. (2015). Fuzzy Rough Decision Trees for Multi-label Classification. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_19
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DOI: https://doi.org/10.1007/978-3-319-25783-9_19
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