Temporal Link Prediction Using Matrix and Tensor Factorizations
ACM Transactions on Knowledge Discovery from Data (TKDD)
Two learning approaches for a rule-based intuitive reasoner
Expert Systems with Applications: An International Journal
Friendship prediction and homophily in social media
ACM Transactions on the Web (TWEB)
An integrated framework for human activity classification
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
An integrated framework for human activity recognition
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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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.