Classification Of Spam Detection Using Naive Bayes Algorithm Over K-Nearest Neighbors Algorithm Based On Accuracy

Main Article Content

A. Gayathri, J. Aswini, A. Revathi

Abstract

AIM: To predict the accuracy percentage of Short Message Services(SMS) spam detection  using machine learning classifiers. Materials and Methods :Two ensemble learning algorithms named naive bayes and k-nearest neighbors are applied to data.The algorithms have been implemented and tested over a dataset which consists of 5574 records. Ensemble learning methods combined several models trained with a given learning algorithm to improve  accuracy. Results and Discussion: After performing the experiment as result shows mean accuracy of 88.05 % by using naive bayes algorithm and compared k-nearest neighbor algorithm mean accuracy is 58.04% for SMS spam detection.There is a statistical significant difference in accuracy for two algorithms is p<0.05 by performing independent samples t-tests. Conclusion: This paper is intended to implement Innovative machine learning classifiers for prediction of SMS spam detection.The comparison results shows that the naive bayes algorithm has appeared to be better performance than k-nearest neighbor algorithm.

Article Details

Section
Articles