Analysis of Classification Model and Feature Subset Selection

In this paper we have investigated three aspects of classifier fusion system applied to the gender classification problem. To find the minimum subset of feature for which the classifier fusion system has better performance, comparison of the three classifier fusion models and the effect of training to testing ratio on the overall output of the classifier fusion system. The final classification results are derived from three models. 1. Single best Model, 2. Fixed combiner, 3. Classifiers as Combiner Model are compared. We have represented the data in DCT domain and features extracted through backward search are given to the classifiers. Evaluation of the classifiers and the fusion models are performed on the basis of ROC Curves and AUCH. Our findings are that in the fixed combiner fusion model the majority voting rule has the best performance. In Single Best Model KNN is the best classifier. The classifier combiner Model has best result for using the minimum set of features.