Abstract: The ‘World Wide Web’, or simply the web, represents one of the largest sources of information in the world. We can say that any topic we think about is probably finding it's on the web. Web information comes in different forms and types such as text documents, images and videos. However, extracting useful information, without the help of some web tools, is not an easy process. Here comes the role of web mining, which provides the tools that help us to extract useful knowledge from data on the internet. Many researchers focus on the issue of web pages classification technology that provides high accuracy. In this paper, several ‘supervised learning algorithms’ evaluation to determining the predefined categories among web documents. We use machine learning algorithms ‘Artificial Neural Networks (ANN)’, ‘Random Forest (RF)’, ‘AdaBoost’ to perform a behavior comparison on the web pages classifications problem.

Ansam A. AbdulHussien, “Comparison of Machine Learning Algorithms to Classify Web Pages” International Journal of Advanced Computer Science and Applications(IJACSA), 8(11), 2017. http://dx.doi.org/10.14569/IJACSA.2017.081127

@article{AbdulHussien2017,
title = {Comparison of Machine Learning Algorithms to Classify Web Pages},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.081127},
url = {http://dx.doi.org/10.14569/IJACSA.2017.081127},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {11},
author = {Ansam A. AbdulHussien}
}