Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
MDL-Based Selection of the Number of Components in Mixture Models for Pattern Classification
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Selection of Classifiers Based on the MDL Principle Using the VC Dimension
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
| Hi-index | 0.01 |
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.