Improved classification techniques by combining KNN and Random Forest with Naive Bayesian classifier
Rajni Devi, P. Sumanjani · 2015
In Recent days, Information Technology walks into all spheres of life. The need for processing the information and analysing the processed information is one of the challenging task in any domain. Naive Bayes is one of the most elegant and simple classifier in data mining field. Irrespective of its feature independence assumptions, it surpasses all other classification techniques by yielding very good performance. In this paper, we attempted to increase the prediction accuracy of Naive Bayes model by integrating it with K nearest neighbours (KNN) and Random forest (RF). We believe that the simplicity of this approach and its great performance will be helpful for any classification.