Naive Bayes Classifier Algorithm for Spam Detection of Email to Improve Accuracy and in Comparison with Decision Tree Algorithm
Keywords:Machine Learning, Supervised Learning, Spam Detection, Spam Filtering, Novel Naive Bayes Classifier, Decision Tree Algorithm.
Aim:The aim of the research is to detect spam in email using the Novel Naive Bayes Classifier (NB) and the Decision Tree algorithm (DT).
Material and Methods: We'll need two groups of 40 samples each to classify spam. The Decision Tree technique (DT) includes a sample size of 20, whereas the Novel Naive Bayes Classifier (NB) includes a sample size of 20 and G-power (value = 0.8).
Results: The accuracy of the Novel Naive Bayes Classifier is 98.05 %, which is higher than the Decision Tree algorithm with 91.80 %. All of us identified that the 2-tailed significant value of accuracy is 0.022 (p<0.05) in the Independent Sample T-Test analysis.
Conclusion: The Novel Naive Bayes Classifier has higher accuracy than the Decision Tree algorithm.