Abstract
Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods.
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Acknowledgment
This research was supported by the National Key Research and Development Program of China (No. 2020YFC0833302), the National Natural Science Foundation of China (No. 62076059), the Science Project of Liaoning Province (2021-MS-105) and the 111 Project (B16009).
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Zhang, K., Liang, W., Cao, P., Yang, J., Li, W., Zaiane, O.R. (2023). Label Correlation Guided Feature Selection for Multi-label Learning. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14179. Springer, Cham. https://doi.org/10.1007/978-3-031-46674-8_27
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DOI: https://doi.org/10.1007/978-3-031-46674-8_27
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