combining classifiers appears as a natural step forward when a critical mass of knowledge of single classifier models has been accumulated. Although there are many unanswered questions about matching classifiers to real-life problems, combining classifiers is rapidly growing and enjoying a lot of attention from pattern recognition and machine learning communities. For any pattern classification task, an increase in data size, number of classes, dimension of the feature space and interclass separability affect the performance of any classifier. It is essential to know the effect of the training dataset size on the recognition performance of a feature extraction method and classifier. In this paper, an attempt is made to measure the performance of the classifier by testing the classifier with two different datasets of different sizes. In practical classification applications, if the number of classes and multiple feature sets for pattern samples are given, a desirable recognition performance can be achieved by data fusion. A framework for feature selection and decision fusion has been proposed in this paper to increase the performance of classification. From the experimental results it is seen that there is an increase of 4.55% in the recognition accuracy.
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