LSSVM-Based Social Spam Detection Model

Xiao Lei Yang; Yi Dan Su; Jin Ping Mo

doi:10.4028/www.scientific.net/AMR.765-767.1281

Paper Titles

The Study Found that the Intelligent Mobile Phone Technology of Malicious Code
p.1263

A Spatial Data Security Model under the Cloud Environment
p.1267

Production Plan of Information Management System for Virtual Industry
p.1271

A Development Model of Semantic Web Application
p.1275

LSSVM-Based Social Spam Detection Model
p.1281

Research on Multiple Spatial Data Gathering Mechanism Based on Geo-Information Browser
p.1287

System Design of the Equipment Support Efficiency Evaluation Simulation System Based on HLA
p.1291

Study on the Inter-Organizational Information System Adaptation Mechanism Based on Organizational Identification
p.1295

Design of SSL VPN System Based on RBAC Access
p.1299

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 765-767LSSVM-Based Social Spam Detection Model

LSSVM-Based Social Spam Detection Model

Abstract:

To Resolve the garbage tag issue in Folksonomy, Lssvm algorithm for social spam detection model (least Squares support vector machine classifiers) was proposed. The method of inequality change the constraints in the traditional support vector machine into equality constraints, and take the empirical function of the squared error loss function as the Experience function in training set. so that the quadratic programming problem convert QP into solving linear equations, it was improving solution the speed of solution and accuracy of convergence.The experimental results show that we have got higher classification accuracyand less predict time than traditional svm detection methods based on least squares support vector machine algorithm garbage tag detection model.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 765-767)

Pages:

1281-1286

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.765-767.1281

Citation:

Cite this paper

Online since:

September 2013

Authors:

Xiao Lei Yang, Yi Dan Su, Jin Ping Mo

Keywords:

Detection Model, LSSVM, Social Bookmark System, Social Spam

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] KIM CJ, HWANG KB. Naive Bayes classier learning with featureselection for spam detection in social bookmarking [c]/ Lecture Notes in Computer Science. Berlin: Springer-Verlag, (2008).

Google Scholar

[2] Tan Xi, Xia Ningxia, Suyi Dan, based on support vector machine garbage tag detection model [J]. Application Research of Computers, 2010, 27(10): 40: 46.

Google Scholar

[3] GRAMME P, CHEVALIER J F. Rank for spam dsetection[c]/Lecture Notes in Computer Science. Berlin: Springer-Verlag, (2008).

Google Scholar

[4] Van Gestel, T., Suykens, J.A. K, Baesens, B, Viaene, S, Vanthienen, J., Dedene, G., De Moor, B. VandewalleJ. Benchmarking least QPuares support vector machine classifiers", Mach. Learning, vol 54, pp.5-32, (2003).

DOI: 10.1023/b:mach.0000008082.80494.e0

Google Scholar

[5] ADKOUR A, HEFNI T, HEFNY A, et al. Using semantic featuresto detect spamming in social bookmarking systems[c]/ LectureNotes in Computer Science. Berlin: Springer-Verlag, (2008).

Google Scholar

[6] HOTHO A, JASCHKE R, SCHMITZ C, et al. Emergent semantics in BibSonomy[M], Liskowsky: GI Jahrestagung, 2006: 305-312.

Google Scholar

[7] SALTON G, McGILL M J. Introduction to modern information retrieval [m]. New York: McGraw-Hill, 1983: 1-12.

Google Scholar

[8] http: /www. csie. ntu. edu. tw/cjlin/libsvmtools/datasets.

Google Scholar

[9] BROADLY. Social spam definition [EB/OL] (2008-7-21). http: /www. bryanchen. com/2008/07/21/social-spam.

Google Scholar

[10] Kuh, A., De Wilde P. Commentson pruning error minimization in least QPuares support vector machines". IEEE Trans,. Neural Networks, vol 18(2). (2007).

DOI: 10.1109/tnn.2007.891590

Google Scholar