Construction and Reduction Methods of Web Spam Identification Index System

Author:

Li Yuancheng1,Huang Rong1,Nie Xiangqian1

Affiliation:

1. School of Control and Computer Engineering, North China Electric Power University, 2 Beinong Road, Huilongguan Town, Changping District, Beijing 102206, China

Abstract

Background: With the rapid development of the Internet, the number of web spam has increased dramatically in recent years, which has wasted search engine storage and computing power on a massive scale. To identify the web spam effectively, the content features, link features, hidden features and quality features of web page are integrated to establish the corresponding web spam identification index system. However, the index system is highly correlation dimension. Methods: An improved method of autoencoder named stacked autoencoder neural network (SAE) is used to realize the reduction of the web spam identification index system. Results: The experiment results show that our method could reduce effectively the index of web spam and significantly improves the recognition rate in the following work. Conclusion: An autoencoder based web spam indexes reduction method is proposed in this paper. The experimental results show that it greatly reduces the temporal and spatial complexity of the future web spam detection model.

Funder

Fundamental Research Funds for the Central Universities

Publisher

Bentham Science Publishers Ltd.

Subject

General Computer Science

Reference32 articles.

1. Fetterly D, Manasse M, Najork M. , Spam, damn spam, and statistics: Using statistical analysis to locate spam Web pagesIn Proceeding 7 International Workshop on the Web and Databases, -,

2. González EJ, , Bentham Science Publishers,, , , Artificial Intelligence Resources in Control and Automation Engineering., 2012,-

3. Goh KL, Singh AK. Procedia Comput Sci, Comprehensive literature review on machine learning structures for web spam classification.,, 2015, 70,, 434-441,

4. Patil RC, D.R. Patil. , Web spam detection using SVM classifierIn: International Conference on Intelligent Systems and Control IEEE, 20151-4,

5. Fdez-Glez J, Ruano-Ordas D, Méndez JR, Fdez-Riverola F, Laza R, Pavón R. Expert Syst Appl, A dynamic model for integrating simple web spam classification techniques.,, 2015, 42,, 7969-7978,

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3