Augmenting Weighted Average with Confusion Matrix to Enhance Classification Accuracy

Accuracy of a classifier or predictor is normally estimated with the help of confusion matrix, which is a useful tool for analyzing how well the classifier can recognize tuples of different classes. Calculation of classification accuracy of a predictor using confusion matrix for two classed attribute is simple. In case of multi classed attribute, we have to take accuracy of all the classes into consideration, to aggregate them to come with the actual accuracy of the particular classifier or predictor for that particular attribute. Here formulating this, weighted average classification accuracy has been introduced for the overall recognition rate of the classifier, which reflects how well the classifier recognizes tuples of various classes. Classification accuracy is being calculated for the classifiers BayesNet(BN), NaiveBayes(NB), J48 and Decision Table(DT) through weighted average accuracy formulation and the trend of the accuracy values for different number of instances is displayed in tables, which shows the flawless calculation.