Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

All submissions of the EM system will be redirected to Online Manuscript Submission System. Authors are requested to submit articles directly to Online Manuscript Submission System of respective journal.
rolex replica best fake rolex replica rolex deneme bonusu veren siteler

Research Article, J Comput Eng Inf Technol Vol: 7 Issue: 1

Random Forests Spam Email Classification System

Khongbantabam Susila Devi*

Raghu Engineering College, Andhra Pradesh, India

*Corresponding Author : Khongbantabam Susila Devi
Raghu Engineering College, Andhra Pradesh, India
Tel: 8974765973
E-mail:
tababi_ning@yahoo.co.in

Received: March 07, 2018 Accepted: March 24, 2018 Published: March 30, 2018

Citation: Devi KS (2018) Random Forests Spam Email Classification System. J Comput Eng Inf Technol 7:1. doi: 10.4172/2324-9307.1000190

Abstract

Email is a rapid communication tool and cost-effective for the users. Conversely, the number of email users driven to the intense growth of spam mails in the past few eras. This spam mails issue is one of the substantial risks with the internet. The growing amount of spam mails brings the importance of trustworthy anti-spam filters. Usually the spammers send the undesired and unsolicited emails to various recipients and these spam mails are mostly identical in its characteristics. Hence it is essential to frame a defense system that effectively finds the spam mails and provide an alternate process for stand-alone filter. Thus, in this paper a novel framework for classifying the email into spam and ham mails using the attribute based random forests classification is proposed. The process begins Bayesian spamminess probability calculation for each token, TF-IDF weighting scheme calculates the weight for each token and the mail, score calculation is performed based on the genetic fitness and finally the classification process is done using random forests classifier to classify the emails into spam and ham emails The results are compared with existing spam classification methods in terms of classification accuracy, weighted accuracy and F1 measure. The results show that the proposed system shows the promising results when compared with other existing algorithms.

Keywords: Spam; E-mail; Digital information; Communication

international publisher, scitechnol, subscription journals, subscription, international, publisher, science

Track Your Manuscript

Awards Nomination
open access