A comparison of classification models to identify the Fragile X Syndrome

DOI10.1080/02664760701832976zbMath1147.62388OpenAlexW2029318760MaRDI QIDQ3532659

A. Pascual-Acosta, Mercedes Carrasco-Mairena, M. D. Cubiles-de-la-Vega, R. Pino-Mejías, J. Muñoz-García

Publication date: 28 October 2008

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664760701832976

zbMATH Keywords

logistic regressionmultilayer perceptronsupport vector machinesclassification treesensemble methodsR systemfragile X syndrome

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50)

Related Items (1)

Applying neural network Poisson regression to predict cognitive score changes

Uses Software

• R
• S-PLUS
• DAAG
• rpart
• e1071
• C4.5

Cites Work

• A decision-theoretic generalization of on-line learning and an application to boosting
• Neural networks: A review from a statistical perspective. With comments and a rejoinder by the authors
• Analyzing bagging
• Population theory for boosting ensembles.
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests
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