Abstract
In recent years, multi-label study has received extensive attention and research in many fields. The feature dimensions of a multi-label data set are high but contain a large amount of noise as well as irrelevant and redundant features. This not only leads to huge storage and time overhead, but also brings serious dimensional disaster problems, making multi-label learning tasks very difficult. Therefore, how to effectively select multi-label features is an important research content in multi-label learning. However, most of the current methods are converted from the methods of single-label feature selection, and feature selection is easy to fall into the local optimal heuristic search strategy. Time complexity has always been the biggest problem of such methods. Based on those considerations, our paper proposes a fast and effective multi-label feature selection method, which uses the optimization strategy to replace the previous search strategy for multi-label feature selection, and transforms the search problem into convex optimization problem. Therefore, the time performance of the traditional method is improved by two to three orders of magnitude. Finally, the experimental results of five evaluation indicators on the four data sets show that our method is superior to many popular methods in feature selection field.
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Acknowledgements
This work is supported by the National Nature Science Foundation of China (No. 61876159, No. 61806172, No. 61572409, No. U1705286 & 61571188), the National Key Research and Development Program of China (No.2018YFC0831402), Fujian Province 2011Collaborative Innovation Center of TCM Health Management, Collaborative Innovation Center of Chinese Oolong Tea Industry-Collaborative Innovation Center (2011) of Fujian Province.
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Lin, P., Sun, Z., Zhang, J., Luo, Z., Li, S. (2019). A Simple and Convex Formulation for Multi-label Feature Selection. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_42
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