Ensemble feature selection with adaptive weights

Chengquan He; Zhuping Li; Haifeng Guo; Mengmeng Li; Donghua Yang; Bo Zheng; Tiansheng Ye; Hongzhi Wang

doi:10.1117/12.3006189

10 October 2023 Ensemble feature selection with adaptive weights

Chengquan He, Zhuping Li, Haifeng Guo, Mengmeng Li, Donghua Yang, Bo Zheng, Tiansheng Ye, Hongzhi Wang

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 1279933 (2023) https://doi.org/10.1117/12.3006189
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Most of the existing ensemble feature selection algorithms directly assign different feature selection algorithms the same weights or simply treat accuracy as weight and then vote for each feature. However, these research methods either ignored or did not fully consider the fitness of the feature selecttion algorithm to the data set. To deal with this challenge, we propose a new ensemble feature selection approach with adaptive weights. In detail, we use the softmax function to dynamically adjust the weight of the base feature selector according to its fitness to dataset in the k-round training process. We name it ensemble feature selection based on softmax function (EFS-BSF) algorithm. We demonstrate the superiority of the EFS-BSF approach over previous methods through mathematical analysis and experiments on multiple data sets.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Chengquan He, Zhuping Li, Haifeng Guo, Mengmeng Li, Donghua Yang, Bo Zheng, Tiansheng Ye, and Hongzhi Wang "Ensemble feature selection with adaptive weights", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 1279933 (10 October 2023); https://doi.org/10.1117/12.3006189

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