Detection and Mitigation of Rare Subclasses in Neural Network Classifiers

11/28/2019 ∙ by Colin Paterson, et al. ∙ 13

Regions of high-dimensional input spaces that are underrepresented in training datasets reduce machine-learnt classifier performance, and may lead to corner cases and unwanted bias for classifiers used in decision making systems. When these regions belong to otherwise well-represented classes, their presence and negative impact are very hard to identify. We propose an approach for the detection and mitigation of such rare subclasses in neural network classifiers. The new approach is underpinned by an easy-to-compute commonality metric that supports the detection of rare subclasses, and comprises methods for reducing their impact during both model training and model exploitation.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 6

page 7

page 8

page 11

page 12

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.