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A new classification method using penalized partial least squares  

Kim, Yun-Dae (Department of Industrial and Management Engineering, POSTECH)
Jun, Chi-Hyuck (Department of Industrial and Management Engineering, POSTECH)
Lee, Hye-Seon (Department of Industrial and Management Engineering, POSTECH)
Publication Information
Journal of the Korean Data and Information Science Society / v.22, no.5, 2011 , pp. 931-940 More about this Journal
Abstract
Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.
Keywords
Classification; logistic regression; partial least squares; penalized function;
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Times Cited By KSCI : 1  (Citation Analysis)
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