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文章基本信息

  • 标题:PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data Classification Problem
  • 本地全文:下载
  • 作者:Adolphus Wagala ; Graciela González-Farías ; Rogelio Ramos
  • 期刊名称:Revista Colombiana de Estadística
  • 印刷版ISSN:2389-8976
  • 出版年度:2020
  • 卷号:43
  • 期号:2
  • 页码:233-249
  • DOI:10.15446/rce.v43n2.81811
  • 出版社:Universidad Nacional de Colombia, sede Bogotá
  • 摘要:This study involves the implentation of the extensions of the partial least squares generalized linear regression (PLSGLR) by combining it with logistic regression and linear discriminant analysis, to get a partial least squares generalized linear regression-logistic regression model (PLSGLR-log), and a partial least squares generalized linear regression-linear discriminant analysis model (PLSGLRDA). A comparative study of the obtained classifiers with the classical methodologies like the k-nearest neighbours (KNN), linear discriminant analysis (LDA), partial least squares discriminant analysis (PLSDA), ridge partial least squares (RPLS), and support vector machines(SVM) is then carried out. Furthermore, a new methodology known as kernel multilogit algorithm (KMA) is also implemented and its performance compared with those of the other classifiers. The KMA emerged as the best classifier based on the lowest classification error rates compared to the others when applied to the types of data are considered; the un- preprocessed and preprocessed.
  • 关键词:Regresíon lineal generalizada;Algoritmo de kernel multilogit;Mínimos cuadrados parciales
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