The efficiency of logistic regression compared to normal discriminant analysis under class-conditional classification noise

Yingtao Bi and Daniel R. Jeske

Journal of Multivariate Analysis, 2010, vol. 101, issue 7, 1622-1637

Abstract: 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.

Keywords: Class; noise; Misclassification; rate; Misspecified; model; Asymptotic; distribution (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (3)

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