A novel combined dynamic ensemble selection model for imbalanced data to detect COVID-19 from complete blood count.

Wu, Jiachao; Shen, Jiang; Xu, Man; Shao, Minglai

Wu, Jiachao; Shen, Jiang; Xu, Man; Shao, Minglai.

Afiliação

Wu J; College of Management and Economics, Tianjin University, Tianjin, 300072, China.
Shen J; College of Management and Economics, Tianjin University, Tianjin, 300072, China.
Xu M; Business School, Nankai University, Tianjin, 300071, China.
Shao M; School of New Media and Communication, Tianjin University, Tianjin, 300072, China. Electronic address: shaoml@tju.edu.cn.

Comput Methods Programs Biomed ; 211: 106444, 2021 Nov.

Article em En | MEDLINE | ID: mdl-34614451

Assuntos

COVID-19; Contagem de Células Sanguíneas; Análise por Conglomerados; Humanos; Aprendizado de Máquina; SARS-CoV-2

Palavras-chave

COVID-19 screening; Candidate classifier generation; Dynamic ensemble selection; Hybrid multiple clustering and bagging; Imbalanced data

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: COVID-19 Limite: Humans Idioma: En Revista: Comput Methods Programs Biomed Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: China

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