An Evaluation of Classification Algorithms Using Mc Nemar's Test

Five classification algorithms namely J48, Naive Bayes, Multi layer Perceptron, IBK and Bayes Net are evaluated using Mc Nemar's test over datasets including both nominal and numeric attributes. It was found that Multi layer Perceptron performed better than the two other classification methods for both nominal and numerical datasets. Furthermore, it was observed that the results of our evaluation concur with Kappa statistic and Root Mean Squared Error, two well-known metrics used for evaluating machine learning algorithms.