Abstract
A genetic algorithm is employed in order to select the appropriate number of components for mixture model classifiers. In this classifier, each class-conditional probability density function can be approximated well using the mixture model of Gaussian distributions. Therefore, the classification performance of this classifier depends on the number of components by nature. In this method, the appropriate number of components is selected on the basis of class separability, while a conventional method is based on likelihood. The combination of mixture models is evaluated by a classification oriented MDL (minimum description length) criterion, and its optimization is carried out using a genetic algorithm. The effectiveness of this method is shown through the experimental results on some artificial and real datasets.
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Tenmoto, H., Kudo, M., Shimbo, M. (2000). Selection of the Number of Components Using a Genetic Algorithm for Mixture Model Classifiers. In: Ferri, F.J., Iñesta, J.M., Amin, A., Pudil, P. (eds) Advances in Pattern Recognition. SSPR /SPR 2000. Lecture Notes in Computer Science, vol 1876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44522-6_53
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DOI: https://doi.org/10.1007/3-540-44522-6_53
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