Improvement of reliability in banknote classification using reject option and local PCA

Abstract

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.