Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimizing the Error/Reject Trade-Off for a Multi-Expert System Using the Bayesian Combining Rule
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
A neural network-based model for paper currency recognition and verification
IEEE Transactions on Neural Networks
Learning data structure from classes: A case study applied to population genetics
Information Sciences: an International Journal
Using the idea of the sparse representation to perform coarse-to-fine face recognition
Information Sciences: an International Journal
The data replication method for the classification with reject option
AI Communications
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The improvement of reliability in banknote neuro-classifier is investigated and a reject option is proposed based on the probability density function of the input data. The classification reliability is evaluated through two reliability parameters, which are associated with the winning class probability and the second maximal probability. Then a threshold value is set up to reject the unreliable classifications. As for modeling the non-linear correlation among the data variables and extracting the features, a local principal components analysis (PCA) is applied. The method is tested with a learning vector quantization (LVQ) classifier using 1440 data samples of various US dollar bills. The results show that by taking a suitable reject threshold value and also a proper number of regions for the local PCA, the reliability of system can be improved significantly.