Robust Classification for Imprecise Environments
Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A lot of randomness is hiding in accuracy
Engineering Applications of Artificial Intelligence
The AUK: A simple alternative to the AUC
Engineering Applications of Artificial Intelligence
Towards automatically detecting whether student learning is shallow
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Improving construct validity yields better models of systematic inquiry, even with less information
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
WTF? detecting students who are conducting inquiry without thinking fastidiously
UMAP'12 Proceedings of the 20th international conference on User Modeling, Adaptation, and Personalization
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
User Modeling and User-Adapted Interaction
Beyond cross-domain learning: Multiple-domain nonnegative matrix factorization
Engineering Applications of Artificial Intelligence
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Receiver operating characteristic (ROC) curves are very powerful tools for measuring classifiers' accuracy in binary-class problems. However, their usefulness in real-world multi-class problems has not been demonstrated yet. In these frequently occurring multi-class cases, simple accuracy meters that do compensate for random successes, such as the kappa statistic, are needed. ROC curves are two-dimensional graphs. Kappa is a scalar. Each comes from an entirely different discipline. This research investigates whether they do have anything in common. A mathematical formulation that links ROC spaces with the kappa statistic is derived here for the first time. The understanding of how these two accuracy meters relate to each other can assist in a better understanding of their respective pros and cons.