An empirical study of univariate and genetic algorithm-based feature selection in binary classification with microarray data

Cancer Informatics

Volumn 2, Issue , 2006, Pages 313-327

  Lecocke, Michael ^a,c   Hess, Kenneth ^b  

^a ST MARY'S UNIVERSITY   (United States)

^b UNIVERSITY OF TEXAS MD ANDERSON CANCER CENTER   (United States)

^c UNIVERSITY OF TEXAS HEALTH SCIENCE CENTER AT SAN ANTONIO   (United States)

Author keywords

Cross validation; Feature selection; Genetic algorithm; Supervised learning

Indexed keywords

ALGORITHM; ARTICLE; BRAIN CANCER; COLON CANCER; CONTROLLED STUDY; EMPIRICISM; GENETIC ALGORITHM; GENETIC ANALYSIS; GENETIC SELECTION; HUMAN; LEUKEMIA; LYMPHOMA; MICROARRAY ANALYSIS; MULTIVARIATE ANALYSIS; PREDICTION; PROSTATE CANCER; UNIVARIATE ANALYSIS; DNA MICROARRAY; FEATURE SELECTION; LEARNING ALGORITHM; OPTIMISM; SELECTION BIAS;

EID: 33750623605     PISSN: 11769351     EISSN: 11769351     Source Type: Journal    
DOI: 10.1177/117693510600200016     Document Type: Article

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