Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to artificial neural systems
Introduction to artificial neural systems
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
The Case against Accuracy Estimation for Comparing Induction Algorithms
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A General Model for Finite-Sample Effects in Training and Testing of Competing Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Estimation of a Priori Decision Threshold for Collocations Extraction: An Empirical Study
International Journal of Information Technology and Web Engineering
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In this study, we investigated the relation between two popular classifier performance measures: area under the receiver operator characteristic curve and overall accuracy. We also evaluated the impact of class imbalance and number of examples in test set on this relation. We perform a set of experiments in which we train multiple neural networks and test them in various, well controlled conditions. The experimental results show that given a large and balanced test set, increase in one performance measure is a very good indicator of increase in the other measure. Furthermore increasing the total number of examples, while keeping the positive class prevalence constant generally increases the correlation between the two measures. Our results also indicate that increasing the extent of class imbalance in the test set has a detrimental effect on this correlation.