Spam Classification Using Deep Learning Technique
DOI:
https://doi.org/10.26438/ijcse/v6i5.383386Keywords:
Spam, Deep Learning, Machine Learning, Classify, WEKAAbstract
Deep Learning technique which is a new area of Machine Learning is showing huge promise in achieving the original goals of Machine learning: Artificial Intelligence. Deep Learning is being applied in every machine learning problem and has shown great results. In this paper, we evaluate the problem of spam classification using Deep Learning Technique and compare the result with other state-of-art machine learning techniques. The machine learning techniques used in the comparison are: Random Forest, Multinomial Naïve Bayesian and Support Vector Machine. The dataset used in the experiment is the CSDMC_2010 and Enron dataset and the platform used is the WEKA interface. Common features are extracted from the body of the spam and feature vector table is constructed, which is used on all the model. Our experiment shows that Deep Learning model outperform all the other machine learning techniques in terms of true positive & true negative and even in the overall accuracy.
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