Paper Title:
Feature Selection Based on GA and PNN
  Abstract

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, S. S. Ma, X. D. Hao, L. L. Ma, L. J. Wang, "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
$32.00
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