Probability Based Genetic Programming for Multiclass Object Classification


Authors: Will Smart, Mengjie Zhang
Source: GZipped PostScript (1075kb); Adobe PDF (306kb)

This paper describes a probability based genetic programming (GP) approach to 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. The approach is examined on three multiclass 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. While the area measure was a bit more effective than the distance measure in most cases, the distance measure was more efficient to learn good program classifiers. Keywords: Probability based genetic programming, Gaussian distribution, overlap area, weighted distribution distance, multiclass object classification.

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