An interactive two-level architecture for a memory network pattern classifier
Pattern Recognition Letters
Handwritten numerical recognition based on multiple algorithms
Pattern Recognition
Model-based chromosome recognition via hypotheses construction/verification
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Filterbank-based fingerprint matching
IEEE Transactions on Image Processing
Improving model accuracy using optimal linear combinations of trained neural networks
IEEE Transactions on Neural Networks
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In this paper we have shown that systematic incorporation of decision from various classifiers following a simple decision decomposition rule, gives better decision in comparison to the existing multiple classifier systems. In our method each classifier were graded according to their effectiveness of providing more accurate results. This approach first utilizes the best classifier. If this classifier classifies the test sample into more than one class or fails to classify the test data then the feature next to the best is summoned to finish up the remaining part of the classification. The continuation of this process, along with the judicious selection of classifiers, yields better efficiency in identifying a single class for the test data. The results obtained after the experiments on a set of fingerprint images shows the effectiveness of our proposed classifier.