Combining the results of several neural network classifiers
Neural Networks
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Adaptive mixtures of local experts
Neural Computation
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In combining classifiers, effort is made to achieve higher accuracy in comparison with the base classifiers that form the ensemble. In this paper, we make modifications to the conventional decision template, DT, method, so that its classification performance is improved in experiments with Satimage, Image Segmentation and Soybean datasets. In our modified version, DT, an elegant strategy in classifier fusion, is used in the first stage of classification task, and in the second stage, the most misclassified classes are directed to a classifier that is specifically devoted to those classes. To identify the most misclassified classes, the confusion matrix of the output of the decision template stage is considered. Experimental results demonstrate the improved performance of the modified version by a 3% increase in the recognition rate for Satimage dataset in comparison with previously published results on Satimage dataset, a 10.57% increase in the recognition rate for Image Segmentation and 4.88% for Soybean dataset, in comparison with the conventional method.