Abstract
Exploiting label correlation is important for multi-label learning, where each instance is associated with a set of labels. However, most of existing multi-label feature selection methods ignore the label correlation. Therefore, we propose a Label Correlation Based Weighting Feature Selection Approach for Multi-Label Data, called MLLCWFS. It is a framework developed from traditional filtering feature selection methods for single-label data. To exploit the label correlation, we compute the importance of each label in mutual information, and adopt three weighting strategies to evaluate the correlation between features and labels. Extensive experiments conducted on four benchmark data sets using two base classifiers demonstrate that our approach is superior to the state-of-the-art feature selection algorithms for multi-label data.
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Acknowledgments
This work is supported in part by the National 973 Program of China under grant 2013CB329604, the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education, China, under grant IRT13059, the Specialized Research Fund for the Doctoral Program of Higher Education under grant 20130111110011, the Natural Science Foundation of China under grants (61503112, 61273292, 61273297, 61229301, 61305063), and the Specified Research Fund for the Doctoral Program of HFUT under grant JZ2015HGBZ0461.
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Liu, L., Zhang, J., Li, P., Zhang, Y., Hu, X. (2016). A Label Correlation Based Weighting Feature Selection Approach for Multi-label Data. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_29
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DOI: https://doi.org/10.1007/978-3-319-39958-4_29
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