an increase in data size, number of classes, dimension of the feature space and interclass separability in any pattern classification task, 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. A desirable recognition performance can be achieved by data fusion in any practical classification applications, if the number of classes and multiple feature sets for pattern samples are given. A framework for feature selection and feature 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 13.20% in the recognition accuracy.

Abstract-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. In this paper, we have proposed a framework based on the combined concepts of decision fusion and feature fusion for the isolated handwritten Kannada numerals classification. The proposed method improves the classification result. From the experimental results it is seen that there is an increase of 13.95% in the recognition accuracy.