Dealing with Class Skew in Context Recognition

Abstract

As research in context recognition moves towards more maturity and real life applications, appropriate and reliable performance metrics gain importance. This paper focuses on the issue of performance evaluation in the face of class skew (varying, unequal occurrence of individual classes), which is common for many context recognition problems. We propose to use ROC curves and Area Under the Curve (AUC) instead of the more commonly used accuracy to better account for class skew. The main contributions of the paper are to draw the attention of the community to these methods, present a theoretical analysis of their advantages for context recognition, and illustrate their performance on a real life case study.