Entropy-based localization of textured regions
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Generating correct compositions of semantic web services with respect to temporal constraints
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Neural Networks
Efficacious end user measures part 1: relative class size and end user problem domains
Advances in Artificial Intelligence - Special issue on Artificial Intelligence Applications in Biomedicine
Bayesian mixed-effects inference on classification performance in hierarchical data sets
The Journal of Machine Learning Research
Discriminant Convex Non-negative Matrix Factorization for the classification of human brain tumours
Pattern Recognition Letters
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Evaluating the performance of a classification algorithm critically requires a measure of the degree to which unseen examples have been identified with their correct class labels. In practice, generalizability is frequently estimated by averaging the accuracies obtained on individual cross-validation folds. This procedure, however, is problematic in two ways. First, it does not allow for the derivation of meaningful confidence intervals. Second, it leads to an optimistic estimate when a biased classifier is tested on an imbalanced dataset. We show that both problems can be overcome by replacing the conventional point estimate of accuracy by an estimate of the posterior distribution of the balanced accuracy.