Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Outlier Detection Using Classifier Instability
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Classifier Conditional Posterior Probabilities
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Transforming classifier scores into accurate multiclass probability estimates
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Confidence-Based Active Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions
IEEE Transactions on Computers
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
Error estimation in pattern recognition via -distance between posterior density functions
IEEE Transactions on Information Theory
The data replication method for the classification with reject option
AI Communications
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Statistical pattern classification techniques have been successfully applied to many practical classification problems. In real-world applications, the challenge is often to cope with patterns that lead to unreliable classification decisions. These situations occur either due to unexpected patterns, i.e., patterns which occur in the regions far from the training data or due to patterns which occur in the overlap region of classes. This paper proposes a method for estimating the reliability of a classifier to cope with these situations. While existing methods for quantifying the reliability are often solely based on the class membership probability estimated on global approximations, in this paper, the reliability is quantified in terms of a confidence interval on the class membership probability. The size of the confidence interval is calculated explicitly based on the local density of training data in the neighborhood of a test pattern. A synthetic example is given to illustrate the various aspects of the proposed approach. In addition, experimental evaluation on real data sets is conducted to demonstrate the effectiveness of the proposed approach to detect unexpected patterns. The lower bound of the confidence interval is used to detect the unexpected patterns. By comparing the performance with the state-of-the-art methods, we show our approach is well-founded.