A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

Zhigao Zeng^1,2, Aoting Tang^1,2, Shengqiu Yi^1,2, Xinpan Yuan^1,2, Yanhui Zhu^1,2,*
1 School of Computer, Hunan University of Technology, Zhuzhou, 412007, China
2 Hunan Key Laboratory of Intelligent Information Perception and Processing Technology, Zhuzhou, 412007, China
* Corresponding Author: Yanhui Zhu. Email: email
(This article belongs to the Special Issue: Recent Advances in Ensemble Framework of Meta-heuristics and Machine Learning: Methods and Applications)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.047989

Received 24 November 2023; Accepted 14 March 2024; Published online 18 April 2024

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Abstract

Radiomics is a non-invasive method for extracting quantitative and higher-dimensional features from medical images for diagnosis. It has received great attention due to its huge application prospects in recent years. We can know that the number of features selected by the existing radiomics feature selection methods is basically about ten. In this paper, a heuristic feature selection method based on frequency iteration and multiple supervised training mode is proposed. Based on the combination between features, it decomposes all features layer by layer to select the optimal features for each layer, then fuses the optimal features to form a local optimal group layer by layer and iterates to the global optimal combination finally. Compared with the current method with the best prediction performance in the three data sets, this method proposed in this paper can reduce the number of features from about ten to about three without losing classification accuracy and even significantly improving classification accuracy. The proposed method has better interpretability and generalization ability, which gives it great potential in the feature selection of radiomics.

Keywords

Radiomics; feature selection; machine learning; metaheuristic

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A Heuristic Radiomics Feature Selection Method Based on Frequency Iteration and Multi-Supervised Training Mode

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