Using Gaussian distribution to construct fitness
functions in genetic programming for multiclass object
classification
Pattern Recognition Letters, 27 (11):
1266--1274 (August 2006)Evolutionary Computer Vision and Image Understanding.DOI: doi:10.1016/j.patrec.2005.07.024
Abstract
the use of Gaussian distribution in genetic
programming (GP) for multiclass object classification
problems. Instead of using predefined multiple
thresholds to form different regions in the program
output space for different classes, this approach uses
probabilities of different classes, derived from
Gaussian distributions, to construct the fitness
function for classification. Two fitness measures,
overlap area and weighted distribution distance, have
been developed. Rather than using the best evolved
program in a population, this approach uses multiple
programs and a voting strategy to perform
classification. The approach is examined on three multi
class object classification problems of increasing
difficulty and compared with a basic GP approach. The
results suggest that the new approach is more effective
and more efficient than the basic GP approach. Although
developed for object classification, this approach is
expected to be able to be applied to other
classification problems.
