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Activity Number: 85 - Machine Learning in Biomedical Data
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: ENAR
Abstract #304551
Title: Classification with Imperfect Training Labels
Author(s): Timothy I. Cannings* and Yingying Fan and Richard Samworth
Companies: University of Edinburgh and University of Southern California and University of Cambridge
Keywords: Classification; noisy data; LDA; QDA; nearest neighbours
Abstract:

Abstract: We study the effect of imperfect training data labels on the performance of classification methods. In a general setting, where the probability that an observation in the training dataset is mislabelled may depend on both the feature vector and the true label, we bound the excess risk of an arbitrary classifier trained with imperfect labels in terms of its excess risk for predicting a noisy label. This reveals conditions under which a classifier trained with imperfect labels remains consistent for classifying uncorrupted test data points. Furthermore, under stronger conditions, we derive detailed asymptotic properties for the popular $k$-nearest neighbour ($k$nn), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) classifiers. One consequence of these results is that the $k$nn and SVM classifiers are robust to imperfect training labels, in the sense that the rate of convergence of the excess risks of these classifiers remains unchanged. On the other hand, the LDA classifier is shown to be typically inconsistent in the presence of label noise unless the prior probabilities of each class are equal.


Authors who are presenting talks have a * after their name.

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