ArticlePeerJ. Computer science2024
Hybrid mRMR and multi-objective particle swarm feature selection methods and application to metabolomics of traditional Chinese medicine.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Machine Learning-Integrated Metabolomics for Precision Pharmacotherapy: Advances, Challenges, and Clinical Translation.Metabolites · 2026Review
- MBLSTM is a contextual interaction refined method for time series prediction.Scientific reports · 2025Article
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Authors and funding
6 authors.
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Abstract
Metabolomics data has high-dimensional features and a small sample size, which is typical of high-dimensional small sample (HDSS) data. Too high a dimensionality leads to the curse of dimensionality, and too small a sample size tends to trigger overfitting, which poses a challenge to deeper mining in metabolomics. Feature selection is a valuable technique for effectively handling the challenges HDSS data poses. For the feature selection problem of HDSS data in metabolomics, a hybrid Max-Relevance and Min-Redundancy (mRMR) and multi-objective particle swarm feature selection method (MCMOPSO) is proposed. Experimental results using metabolomics data and various University of California, Irvine (UCI) public datasets demonstrate the effectiveness of MCMOPSO in selecting feature subsets with a limited number of high-quality features. MCMOPSO achieves this by efficiently eliminating irrelevant and redundant features, showcasing its efficacy. Therefore, MCMOPSO is a powerful approach for selecting features from high-dimensional metabolomics data with limited sample sizes.
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