SMS Spam Detection Using Multinational Naive Bayes Algorithm Compared with Decision Tree Algorithm

Abstract

Aim: The main objective of this research is to improve accuracy through machine learning algorithms. Multinational Naive Bayes Algorithm and Decision Tree Algorithm were used in this research. Materials and Methods: Detection is performed by Multinational Naive Bayes Algorithm (N=10) over Decision Tree Algorithm (N=10). Sample size is calculated using GPower with pretest power as 0.8 and alpha 0.05. Result: Mean performance of Multinational Naive Bayes Algorithm (97.80%) is high compared to Decision Tree Algorithm (96.50%). Significance value for performance and loss is 0.398 (P>0.536). Conclusion: The mean performance of a Novel SMS spam detection using Multinational Naive Bayes Algorithm is better than Decision Tree Algorithm.