TY  - JOUR
T1  - Web Page Block Identification using Machine Learning Techniques
AU - Narwal, Neetu AU - Kumar Sharma, Sanjay AU - Prakash Singh, Amit 
JO  - International Journal of System Signal Control and Engineering Application
VL  - 12
IS  - 4
SP  - 67
EP  - 73
PY  - 2019
DA  - 2001/08/19
SN  - 1997-5422
DO  - ijssceapp.2019.67.73
UR  - https://makhillpublications.co/view-article.php?doi=ijssceapp.2019.67.73
KW  - DOM
KW  -page block
KW  -radial basis network
KW  -support vector machine
KW  -neural network
AB  - Internet has gained greatest acceptance as
reservoirs of information. It has been observed that the
web page along with main content comprises of noise
(advertisement, external links). This noise content poses
difficulty for various search engines to classify the web
page accurately and provides distraction to the serious
user interested in gathering data related to a topic. There
are various segmentation techniques that partition the web
page but very few have categorized the segmented block.
In this study, we tried to categorize the page blocks
extracted from segmentation. We have used web page
segmentation algorithm for parsing the web page and
extracted important features to build a dataset. Linear and
nonlinear machine learning techniques to have been used
to train dataset. In this experiment we also analyzed the
importance of features for the learning process. We
perceived that the embedded objects from external source
have highest significance for block identification. In our
experiment, the non-linear radial basis neural network
resulted in best performance with an accuracy of 99.89%.
ER  -