Abstract
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.
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Smart, W., Zhang, M. (2004). Probability Based Genetic Programming for Multiclass Object Classification. In: Zhang, C., W. Guesgen, H., Yeap, WK. (eds) PRICAI 2004: Trends in Artificial Intelligence. PRICAI 2004. Lecture Notes in Computer Science(), vol 3157. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28633-2_28
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DOI: https://doi.org/10.1007/978-3-540-28633-2_28
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Print ISBN: 978-3-540-22817-2
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