A hybrid approach for improving the classification performance

Abstract

There are many factors that affect the performance of classification. The volume, size, type of data and classification methods are the most obvious factors. For the exact same data set, it is possible to achieve different classification performance values by using different classification methods Hence, the development of classification models that are more accurate and applicable to many areas for classification problems has a great importance. In this work, a hybrid classification model combining Naïve Bayes, Perceptron and KNN is proposed. In this model, a novel parameter called Decision Function is used. The proposed decision function aims to increase the classification success by considering the classification results of the three algorithms The performance evaluation results show that the proposed decision function significantly improves the classification success.