Ting Cai1, Chun Ye1, Zhiwei Ye1,*, Ziyuan Chen1, Mengqing Mei1, Haichao Zhang1, Wanfang Bai2, Peng Zhang3
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1157-1175, 2024, DOI:10.32604/cmc.2024.055080
- 15 October 2024
Abstract The world produces vast quantities of high-dimensional multi-semantic data. However, extracting valuable information from such a large amount of high-dimensional and multi-label data is undoubtedly arduous and challenging. Feature selection aims to mitigate the adverse impacts of high dimensionality in multi-label data by eliminating redundant and irrelevant features. The ant colony optimization algorithm has demonstrated encouraging outcomes in multi-label feature selection, because of its simplicity, efficiency, and similarity to reinforcement learning. Nevertheless, existing methods do not consider crucial correlation information, such as dynamic redundancy and label correlation. To tackle these concerns, the paper proposes a More >
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