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Efficient Content Based Spam Filtering using Bayesian Method

Author(s):

Himanshu Gupta , MANAV RACHNA INTERNATIONAL UNIVERSITY; Vasudha Arora, MANAV RACHNA INTERNATIONAL UNIVERSITY

Keywords:

spam mails, non-spam mails, Naïve Bayes Classifier, learning dataset, laplacian, stop_words, ignore_words

Abstract

There are two types of emails one in wanted emails from authorised users and other is unsolicited or unwanted emails, these unsolicited emails are called spams. This is a rapidly growing problem in the domain of emails these days. Emails are used by millions of people and send billions of mails daily all over the world. Over the last 1.5 decade it has become a very big problem. Every day a very huge amount of spam emails are received by the users. Due to this the business in not only this industry but also in every domain loses productivity, and it costs billions of dollars. It also strains the IT infrastructure. These emails become very frustrating for the users. So we need an efficient method to filter these mails and its necessity is increasing day by day. In this paper, we represent a method to do the same based on Naïve Bayes Classifier. It works by evaluating the probability of occurrence of a keyword in spam and in legitimate emails and classify by comparing them.

Other Details

Paper ID: IJSRDV3I30470
Published in: Volume : 3, Issue : 3
Publication Date: 01/06/2015
Page(s): 1048-1051

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