Selection of the Number of Components Using a Genetic Algorithm for Mixture Model Classifiers
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.
Keywordsmixture model classifier class-conditional probability density function class separability minimum description length criterion genetic algorithm
- 3.Tenmoto, H., Kudo, M., Shimbo, M.: Determination of the Number of Components Based on Class Separability in Mixture-Based Classifiers. Proceedings of the Third International Conference on Knowledge-Based Intelligent Information Engineering Systems. (1999) 439–442Google Scholar
- 4.Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press (1975)Google Scholar
- 7.Kudo, M., Shimbo, M.: Selection of Classifiers Based on the MDL Principle Using the VC Dimension. Proceedings of the 11th International Conference on Pattern Recognition. (1996) 886–890.Google Scholar
- 9.Murphy, P. M., Aha, D.W.: UCI Repository of Machine Learning Databases [Machine-Readable Data Repository]. University of California Irvine, Department of Information and Computation Science. (1996)Google Scholar