A Scalable Method for Improving the Performance of Classifiers in Multiclass Applications by Pairwise Classifiers and GA

In this paper, a new combinational method for improving the recognition rate of multiclass classifiers is proposed. The main idea behind this method is using pairwise classifiers to enhance the ensemble. Because of more accuracy of them, they can decrease the error rate in error-prone feature space. Firstly, a multiclass classifier has been trained. Then, regarding to confusion matrix and evaluation data, the pair-classes that have the most error have been derived. After that, pairwise classifiers have been trained and added to ensemble of classifiers. Finally, weighted majority vote for combining the primary results is applied. In this paper, multi layer perceptron is used as base classifier. Also, GA determines the optimized weights in final classifier. This method is evaluated on a Farsi digit handwritten dataset. Using proposed method, the recognition rate of simple multiclass classifier has been improved from 97.83 to 98.89 which shows an adequate improvement.

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