TY  - JOUR
T1  - Web Spam Detection and Classification using Hybrid Extensive Machine
Learning Algorithm (HEMLA) for Domain Specific Features
AU - Muralidharan, T. AU - Raja, V. Saishanmuga AU - Rajagopalan, S.P. 
JO  - Asian Journal of Information Technology
VL  - 16
IS  - 7
SP  - 632
EP  - 638
PY  - 2017
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2017.632.638
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2017.632.638
KW  - Simple mail transfer protocol
KW  -back-propagation
KW  -information
KW  -spam
KW  -filtering methods
KW  -supply route
AB  - In the most recent couple of years as internet utilization turns into the principle supply route of the
life’s every day exercises, the issue of spam turns out to be intense for web group. Spam pages frame a genuine
risk for a wide range of clients. This risk demonstrated to advance constantly with no piece of information to
lessen. Diverse types of spam saw an emotional increment in both size and negative effect. A lot of e-mails and
website pages are considered spam either in Simple Mail Transfer Protocol (SMTP) or web crawlers. Numerous
specialized strategies were proposed to approach the issue of spam. We propose a Hybrid Extensive Machine
Learning Algorithm (HEMLA) for detection and classification of that offers weight to the data nourished by
clients and thinks about the presence of some space particular components. Hybrid extensive machine learning
algorithm is a combination of many learning algorithms like conjugate gradient, resilient back-propagation and
levenberg-marquardt algorithms. The outcomes demonstrate that the hybrid extensive machine learning
algorithm overcomes the traditional web filtering methods as far as reducing the false positives and the false
negatives and increasing the accuracy.
ER  -