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@article{146306,
        author = {Raveena. K and K. Chandra Prabha},
        title = {CONTENT BASED SPAM FILTERING IN EMAIL USING NAIVE BAYES CLASSIFIER},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {12},
        pages = {127-130},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=146306},
        abstract = {Email spam keeps on turning into an  
issue on the Internet. Spammed email may contain numerous duplicates of a similar message, business commercial or other insignificant posts like obscene substance. In past research, distinctive filtering methods are utilized to identify these messages, for example, utilizing Random Forest, Naive Bayesian, Support Vector Machine (SVM) and Neutral Network. In this examination, we test Naive Bayes calculation for email spam filtering on two informational collections and test its execution, i.e., Spam Data and SPAM BASE informational indexes. The execution of the informational indexes is assessed in view of their exactness, review, accuracy and F-measure. Our exploration utilize WEKA instrument for the assessment of Naive Bayes calculation for email spam filtering on the two informational collections. The outcome demonstrates that the kind of email and the quantity of examples of the informational index has an impact towards the execution of Naive Bayes.
},
        keywords = {spam filters, naive spam, content based spam},
        month = {},
        }