Multilayer perceptrons neural network based web spam detection application

Web spam detection is a crucial task due to its devastationtowards Web search engines and global cost of billiondollars annually. For these reasons, a multilayeredperceptrons (MLP) neural network is presented in this paperto improve the Web spam detection accuracy. MLP neuralnetwork is used for Web...

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Main Authors: Goh, K.L., Singh, Ashutosh Kumar, Lim, King Hann
Other Authors: IEEE
Format: Conference Paper
Published: IEEE 2013
Subjects:
Online Access:http://hdl.handle.net/20.500.11937/40496
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author Goh, K.L.
Singh, Ashutosh Kumar
Lim, King Hann
author2 IEEE
author_facet IEEE
Goh, K.L.
Singh, Ashutosh Kumar
Lim, King Hann
author_sort Goh, K.L.
building Curtin Institutional Repository
collection Online Access
description Web spam detection is a crucial task due to its devastationtowards Web search engines and global cost of billiondollars annually. For these reasons, a multilayeredperceptrons (MLP) neural network is presented in this paperto improve the Web spam detection accuracy. MLP neuralnetwork is used for Web spam classification due to itsflexible structure and non-linearity transformation toaccommodate latest Web spam patterns. An intensiveinvestigation is carried out to obtain an optimal number ofhidden neurons. Both Web spam link-based and contentbasedfeatures are fed into MLP network for classification.Two benchmarking datasets – WEBSPAM-UK2006 andWEBSPAM-UK2007 are used to evaluate the performanceof the proposed classifier. The overall performance iscompared with the state of the art support vector machine(SVM) which is widely used to combat Web spam. Theexperiments have shown that MLP network outperformsSVM up to 14.02% on former dataset and up to 3.53% onlater dataset.
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spelling curtin-20.500.11937-404962017-09-13T13:37:32Z Multilayer perceptrons neural network based web spam detection application Goh, K.L. Singh, Ashutosh Kumar Lim, King Hann IEEE Web Spam Support Vector Machines Neural Network Scaled Conjugate Gradient Web spam detection is a crucial task due to its devastationtowards Web search engines and global cost of billiondollars annually. For these reasons, a multilayeredperceptrons (MLP) neural network is presented in this paperto improve the Web spam detection accuracy. MLP neuralnetwork is used for Web spam classification due to itsflexible structure and non-linearity transformation toaccommodate latest Web spam patterns. An intensiveinvestigation is carried out to obtain an optimal number ofhidden neurons. Both Web spam link-based and contentbasedfeatures are fed into MLP network for classification.Two benchmarking datasets – WEBSPAM-UK2006 andWEBSPAM-UK2007 are used to evaluate the performanceof the proposed classifier. The overall performance iscompared with the state of the art support vector machine(SVM) which is widely used to combat Web spam. Theexperiments have shown that MLP network outperformsSVM up to 14.02% on former dataset and up to 3.53% onlater dataset. 2013 Conference Paper http://hdl.handle.net/20.500.11937/40496 10.1109/ChinaSIP.2013.6625419 IEEE restricted
spellingShingle Web Spam
Support Vector Machines
Neural Network
Scaled Conjugate Gradient
Goh, K.L.
Singh, Ashutosh Kumar
Lim, King Hann
Multilayer perceptrons neural network based web spam detection application
title Multilayer perceptrons neural network based web spam detection application
title_full Multilayer perceptrons neural network based web spam detection application
title_fullStr Multilayer perceptrons neural network based web spam detection application
title_full_unstemmed Multilayer perceptrons neural network based web spam detection application
title_short Multilayer perceptrons neural network based web spam detection application
title_sort multilayer perceptrons neural network based web spam detection application
topic Web Spam
Support Vector Machines
Neural Network
Scaled Conjugate Gradient
url http://hdl.handle.net/20.500.11937/40496