Supervised Relevance-Redundancy assessments for feature selection in omics-based classification scenarios.
Journal of biomedical informatics(2023)
摘要
ReRa approach has the potential to improve the performance of machine learning models used in an unbalanced classification scenario. Compared to another Relevance-Redundancy approach like MRmr, ReRa does not require tuning the number of preserved features, ensures efficiency and scalability over huge initial dimensionalities and allows re-evaluation of all previously selected features at each iteration of the redundancy assessment, to ultimately preserve only the most relevant and class-differentiated features.
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关键词
Feature selection, Unbalanced classification, Clinically-relevant stratification, Relevance-redundancy strategies, Transcript isoforms
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