Feature Selection Based on GA and PNN

Abstract:

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A new image feature selection method with the combination of Genetic Algorithm(GA) and Probabilistic Neural Network(PNN) is proposed and applied to potato shape feature selection and classification. The classifier selecting principle is investigated by combining with the genetic algorithm. A new feature selection method based on GA and PNN is put forward firstly. Comprehensively considering the factor of classification accuracy,selected feature number and the impact of the two factors, a new fitness function is proposed. The initial Zernike moments parameters of potatoes are optimized using improved genetic algorithm, and nineteen Zernike moments are extracted to form the shape feature. The shape detection accuracy can reach 93% and 100% respectively for the perfect and malformation potatoes. The tests indicate that the fitness function and feature selection method can be used for searching the best feature combination.

Info:

Periodical:

Advanced Materials Research (Volumes 217-218)

Edited by:

Zhou Mark

Pages:

1753-1757

DOI:

10.4028/www.scientific.net/AMR.217-218.1753

Citation:

M. Hao et al., "Feature Selection Based on GA and PNN", Advanced Materials Research, Vols. 217-218, pp. 1753-1757, 2011

Online since:

March 2011

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Price:

$35.00

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