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Multiple reference points-based multi-objective feature selection for multi-label learning

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Abstract

In the real world, data often exhibits high-dimensional and complex characteristics. In addition, an object may correspond to multiple class labels. Therefore, filtering and processing such data has become a hot research topic. Multi-label feature selection is one of the key preprocessing techniques for effectively solving redundant information. However, due to the high complexity of multi-label learning feature selection and involving optimization of multiple objectives, careful consideration is required to achieve effective optimization. To address these issues, this paper proposes a multiple reference points-based multi-objective feature selection for multi-label learning. Firstly, the neighborhood crossover strategy utilizes individuals in the same neighborhood for crossover, thereby enhancing the search effect within that decision space. The distribution-based mutation strategy adjusts the mutation probability of different features based on their distribution in selected candidate solutions to increase the likelihood of potentially useful features being selected. Secondly, the multiple reference points-based selection strategy uses a scalar function to uniformly evaluate offspring individuals and control their distance from each other through multiple reference points. This approach helps in selecting convergent solutions with balanced diversity. Finally, experimental results demonstrate that the proposed algorithm attains an optimal performance of 83% across fourteen widely used datasets, outperforming ten state-of-the-art algorithms in terms of effectiveness and performance in multi-label feature selection.

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Funding

This work is supported by the National Natural Science Foundation of China (No.62366019 and No.61966016), and the Natural Science Foundation of Jiangxi Province, China (No.20224BAB202020).

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Authors and Affiliations

  1. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, 341000, China

    Yangtao Chen

  2. School of Software, Jiangxi Agricultural University, Nanchang, 330045, China

    Wenbin Qian

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  1. Yangtao Chen
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Yangtao Chen: Software, Writing - Original draft. Wenbin Qian: Methodology, Conceptualization, Writing-review and editing.

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Correspondence to Wenbin Qian.

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Chen, Y., Qian, W. Multiple reference points-based multi-objective feature selection for multi-label learning. Appl Intell 54, 4952–4978 (2024). https://doi.org/10.1007/s10489-024-05387-0

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