Barley seeds classification with a genetically optimized kernel density estimator

Authors:
Vasile Gui;Florin Alexa;Catalin Caleanu
Affiliations:
Department of Electronics and Telecommunications, "Politehnica" University Timisoara, Timisoara, Romania;Department of Electronics and Telecommunications, "Politehnica" University Timisoara, Timisoara, Romania;Department of Electronics and Telecommunications, "Politehnica" University Timisoara, Timisoara, Romania
Venue:
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Year:
2007

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Abstract

This paper summarizes our new research on a unified design of the feature extraction and classification processes. We have developed a multiple class classifier, based on a genetically optimized kernel density estimator. The genetic algorithm provides the tool to solve the bandwidth matrix optimization problem. The bandwidth matrix of the kernel function plays a similar role with the matrix used in a linear feature extraction, since it weights differently the data vector components. More, the bandwidth matrix controls the smoothness of decision surfaces. Tests are made with both, the standard nonparametric k-Nearest Neighbour classifier and the genetically optimized kernel based density estimator. Results on a barley seed image feature data show the utility of the proposed approach.