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.
Highlights
Achieving the best possible classification performance for a given problem domain has become the ultimate goal of designing the pattern recognition systems
If the number of classes and multiple feature sets for pattern samples are given, a desirable recognition performance can be achieved based on these sets of features using data fusion [2]
The curvelet transform is used to extract the features from the numeral samples in the dataset1.All the five different subsets of features extracted are applied on this dataset and classified
Summary
Achieving the best possible classification performance for a given problem domain has become the ultimate goal of designing the pattern recognition systems. This objective traditionally led to the development of different classification schemes for any pattern recognition problem to be solved. Studies have been done to obtain the optimal feature set and classifier set. Features play a very important role for any pattern classification task. A set of bad features can deteriorate the performance of a good classifier. With the increase in noise and dimensionality, feature selection becomes an essential step
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