کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1146551 | 957517 | 2010 | 16 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: The efficiency of logistic regression compared to normal discriminant analysis under class-conditional classification noise The efficiency of logistic regression compared to normal discriminant analysis under class-conditional classification noise](/preview/png/1146551.png)
In many real world classification problems, class-conditional classification noise (CCC-Noise) frequently deteriorates the performance of a classifier that is naively built by ignoring it. In this paper, we investigate the impact of CCC-Noise on the quality of a popular generative classifier, normal discriminant analysis (NDA), and its corresponding discriminative classifier, logistic regression (LR). We consider the problem of two multivariate normal populations having a common covariance matrix. We compare the asymptotic distribution of the misclassification error rate of these two classifiers under CCC-Noise. We show that when the noise level is low, the asymptotic error rates of both procedures are only slightly affected. We also show that LR is less deteriorated by CCC-Noise compared to NDA. Under CCC-Noise contexts, the Mahalanobis distance between the populations plays a vital role in determining the relative performance of these two procedures. In particular, when this distance is small, LR tends to be more tolerable to CCC-Noise compared to NDA.
Journal: Journal of Multivariate Analysis - Volume 101, Issue 7, August 2010, Pages 1622–1637