TY  - JOUR
T1  - Spam Classification by using Naive Bayes Algorithm Based on Segmentation
AU - Suhail Najam, Shahad AU - Hashim AL-Saedi, Karim 
JO  - Research Journal of Applied Sciences
VL  - 14
IS  - 12
SP  - 437
EP  - 447
PY  - 2019
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2019.437.447
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2019.437.447
KW  - Spam
KW  -Naive Bayes
KW  -information gain
KW  -term frequency invers term frequency
AB  - One of the greatest methods of communication
convenient involves using e-mail for personal messages
or commercial objective. Being one of the strongest and
quick ways of communication, email’s publicity has led
to increased undesirable spam email. Email spam is one
of the main problems of the Internet today and bringing
financial damage to companies and individual users.
Spam mails can be harmful as they may contain malware
and links to phishing Web sites. So, necessary to separate
spam from mail messages into a separate folder. Filter
classification can be classified in two techniques-learning
method based on machine learning techniques and
non-machine techniques. Most popular machine learning
techniques due to the high accuracy and athletic support.
Machine learning techniques include Naive Bayes and
support vector machine learning and decision tree, etc.
while non-machine learning techniques, black and white
list, signatures and verify email address and mail header
checking, etc. In this study utilize one of mechanism
learning techniques is Naive Bayes algorithm and for
extract features from dataset used Term Frequency
Invers Term Frequency (TFIDF) method. For reduce
dimensionality of feature space use Information Gain (IG)
method.
ER  -