Employing optimized combinations of one-class classifiers for automated currency validation

Automated currency validation requires a decision to be made regarding the authenticity of a banknote presented to the validation system. This decision often has to be made with little or no information regarding the characteristics of possible counterfeits as is the case for issues of new currency. A method for automated currency validation is presented which segments the whole banknote into different regions, builds individual classifiers on each region and then combines a small subset of the region specific classifiers to provide an overall decision. The segmentation and combination of region specific classifiers to provide optimized false positive and false negative rates is achieved by employing a genetic algorithm. Experiments based on high value notes of Sterling currency were carried out to assess the effectiveness of the proposed solution.

[1]  Carmen García-Mateo,et al.  On combining classifiers for speaker authentication , 2003, Pattern Recognit..

[2]  Ponnuthurai N. Suganthan.  Pattern classification using multiple hierarchical overlapped self-organising maps , 2001, Pattern Recognit..

[3]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[4]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[5]  Ray L. Somorjai,et al.  Application of Several Methods of Classification Fusion to Magnetic Resonance Spectra , 1996, Connect. Sci..

[6]  Yann Guermeur,et al.  Combining Discriminant Models with New Multi-Class SVMs , 2002, Pattern Analysis & Applications.

[7]  Ludmila I. Kuncheva,et al.  Feature Subsets for Classifier Combination: An Enumerative Experiment , 2001, Multiple Classifier Systems.

[8]  Stephan R. Sain,et al.  A New Test for Outlier Detection from a Multivariate Mixture Distribution , 1997 .

[9]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[10]  Josef Schmee,et al.  An introduction to applied multivariate statistics , 1984 .

[11]  William B. Langdon,et al.  Data Fusion by Intelligent Classifier Combination , 2001 .

[12]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[13]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[14]  William B. Langdon,et al.  Genetic programming for combining classifiers , 2001 .

[15]  David M. J. Tax,et al.  One-class classification , 2001 .

[16]  Melanie Mitchell,et al.  An introduction to genetic algorithms , 1996 .

[17]  Bogdan Gabrys,et al.  Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting , 2001, Multiple Classifier Systems.

[18]  Peter A. N. Bosman,et al.  Proceedings of the Genetic and Evolutionary Computation Conference - GECCO - 2006 , 2006 .

[19]  David Yarowsky,et al.  Modeling Consensus: Classifier Combination for Word Sense Disambiguation , 2002, EMNLP.

[20]  Robert P. W. Duin,et al.  Combining One-Class Classifiers , 2001, Multiple Classifier Systems.

[21]  S. Roberts.  Novelty detection using extreme value statistics , 1999 .

[22]  M. Narasimha Murty,et al.  Off-line signature verification using genetically optimized weighted features , 1999, Pattern Recognit..

[23]  D. F. Morrison,et al.  Multivariate Statistical Methods , 1968 .

[24]  Bernhard Schölkopf,et al.  Support Vector Novelty Detection Applied to Jet Engine Vibration Spectra , 2000, NIPS.

[25]  E. Ziegel.  Introduction to the Theory and Practice of Econometrics , 1989 .