MISCLASSIFICATION AMONG METHODS USED FOR MULTIPLE GROUP DISCRIMINATION - THE EFFECTS OF DISTRIBUTIONAL PROPERTIES
MISCLASSIFICATION AMONG METHODS USED FOR MULTIPLE GROUP DISCRIMINATION - THE EFFECTS OF DISTRIBUTIONAL PROPERTIES
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DOI：
10.1002/sim.4780100511
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发表时间：
1991-05-01
影响因子：
2
通讯作者：
BARON, AE
中科院分区：
文献类型：
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作者：
BARON, AE
当协变量分布为正态分布或非正态分布时，多组判别分析方法在分类为两个以上总体方面尚未得到充分研究。本研究考察了几种多重判别方法在各种模拟的连续正态和非正态协变量分布下的分类性能。这些方法包括多分类Logistic回归、多组线性判别分析、核密度估计以及作为线性函数输入的数据的秩变换。感兴趣的参数是种群间的距离、种群平均向量的配置(共线或形成规则单纯形的顶点)、偏度、峰度和双峰性。最后三个参数的模拟分别采用对数正态分布、Sinh-1正态分布和正态分布的双分量混合分布。三个三变量总体的结果表明，在Neyman-Pearson分配下，对于所有分布，Logistic判别分类接近最优。这些结果表明，对于非正态数据的多组分类，Logistic判别法优于其他广泛使用的方法，与正态数据的多重线性判别法相当。
Methods of multiple group discriminant analysis have not been fully studied with respect to classification into more than two populations when the covariate distributions are normal or non-normal. The present study examines the classification performance of several multiple discrimination methods under a variety of simulated continuous normal and non-normal covariate distributions. The methods include polychotomous logistic regression, multiple group linear discriminant analysis, kernel density estimation, and rank transformations of the data as input into the linear function. The parameters of interest were distance among populations, configuration of population mean vectors (collinear or forming the vertices of a regular simplex), skewness, kurtosis and bimodality. Simulation of the last three parameters was by log-normal, sinh-1 normal and a two-component mixture of normal distributions, respectively. Results with three trivariate populations show that for all distributions, logistic discrimination classifies close to the optimal under Neyman-Pearson allocation. These results suggest that logistic discrimination is preferable to other widely-used methods for multiple group classification with non-normal data, and is comparable to classification by multiple linear discrimination with normal data.