벌점함수가 적용된 부분최소자승법을 이용한 분류 방법

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. On the base of those methods, this paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust from noise and remedy multicollinearity problems. This paper compares the proposed method with logistic regression and discriminant analysis by some real data and artificial data. It is concluded that the new method has better power as compared with other methods.