The Effects of Different Response Values in Linear Regression Model on Binary Classification
Abstract: We use the multiple linear regression model to deal with the classification problem of two popula-tions. Firstly, we assign the response variables and some corresponding values with certain rules, and then construct discriminant function and criterion via least square method. On this basis, we discuss the effects of different response values on classification for balanced and unbalanced data in linear model. In addition, we compare the mentioned discriminant method above with classic discriminant methods including the classical Mahalanobis distance discriminant and Bayes dis-criminant. At last, we find the inner relation between these methods as well as their advantages and disadvantages.
文章引用: 王小英 , 杨岩丽 , 陈常龙 (2015) 线性回归模型中响应值的选取对二分类问题的影响。 统计学与应用， 4， 47-55. doi: 10.12677/SA.2015.42007
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