Abstract
Recently, multi-label learning is concerned and studied in lots of fields by many researchers. However, multi-label datasets often have noisy, irrelevant and redundant features with high dimensionality. Accompanying with these issues, a critical challenge called “the curse of dimensionality” makes many tasks of multi-label learning very difficult. Therefore, many method such as feature selection to solve this problem has received much attention. Among many feature selection methods, a large number of information-theoretical-based methods are developed to solve the learning issue and the results are very good. Unfortunately, most of existing feature selection methods are either directly transformed from single-label methods or insufficient in light of using heuristic algorithms as the search component. Motivated by this, a novel fast method based on mutual information with no parameter is proposed, which obtains the optimal solution via constrained convex optimization with less time. Specifically, by incorporating the label information into the feature selection process, label-correlation is taken into consideration to generate the generalized model.
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Sun, Z., Zhang, J., Luo, Z., Cao, D., Li, S. (2019). A Fast Feature Selection Method Based on Mutual Information in Multi-label Learning. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_31
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DOI: https://doi.org/10.1007/978-981-13-3044-5_31
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