Volume 11 - Volume 11
Higher Classification of Fake Political News Using Decision Tree Algorithm Over Naive Bayes Algorithm
Abstract
Aim: The main aim of the study proposed is to perform higher classification of fake political news
by implementing fake news detectors using machine learning classifiers by comparing their
performance. Materials and Methods: By considering two groups such as Decision Tree algorithm
and Naive Bayes algorithm. The algorithms have been implemented and tested over a dataset which
consists of 44,000 records. Through the programming experiment which is performed using N=10
iterations on each algorithm to identify various scales of fake news and true news classification.
Result: After performing the experiment the mean accuracy of 99.6990 by using Decision Tree algorithm and the accuracy of 95.3870 by using Naive Bayes algorithm for fake political news in.
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 the innovative fake news detection approach on recent Machine Learning Classifiers for prediction of fake political news. By testing the algorithms performance and accuracy on fake political news detection and other issues. The comparison results shows that the Decision Tree algorithm has better performance
when compared to Naive Bayes algorithm.
Paper Details
PaperID: 1738
Author's Name: T. Dinesh and Dr.T. Rajendran
Volume: Volume 11
Issues: Volume 11
Keywords: nnovative Fake News Detection, Decision Tree Algorithm, Naive Bayes Algorithm, Machine Learning, Statistical Analysis.
Year: 2021
Month: April
Pages: 1084-1096