Web Spam Classification Using Supervised Artificial Neural Network Algorithms

Web Spam Classification Using Supervised Artificial Neural Network Algorithms

Ashish Chandra, Mohammad Suaib and Rizwan Beg Integral University, India

ABSTRACT

Due to the rapid growth in technology employed by the spammers, there is a need of classifiers that aremore efficient, generic and highly adaptive. NeuralNetwork based technologies have high ability ofadaption as well as generalization. As per our knowledge, very little work has been done in this fieldusingneural network. We present this paper to fill thisgap. This paper evaluates performance of three supervisedlearning algorithms of artificial neural network bycreating classifiers for the complex problem of latestweb spam pattern classification. These algorithms are Conjugate Gradient algorithm, Resilient Back-propagation learning, and Levenberg-Marquardt algorithm

KEYWORDS Web spam, artificial neural network, back-propagation algorithms, Conjugate Gradient, Resilient Back-propagation, Levenberg-Marquardt, Web spam classification Original Source URL: http://airccse.org/journal/acii/papers/2115acii02.pdf http://airccse.org/journal/acii/vol2.html