Call for Paper - July 2023 Edition
IJCA solicits original research papers for the July 2023 Edition. Last date of manuscript submission is June 20, 2023. Read More

Machine Learning: A Review on Binary Classification

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Roshan Kumari, Saurabh Kr. Srivastava

Roshan Kumari and Saurabh Kr. Srivastava. Machine Learning: A Review on Binary Classification. International Journal of Computer Applications 160(7):11-15, February 2017. BibTeX

	author = {Roshan Kumari and Saurabh Kr. Srivastava},
	title = {Machine Learning: A Review on Binary Classification},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2017},
	volume = {160},
	number = {7},
	month = {Feb},
	year = {2017},
	issn = {0975-8887},
	pages = {11-15},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2017913083},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


In the field of information extraction and retrieval, binary classification is the process of classifying given document/account on the basis of predefined classes. Sockpuppet detection is based on binary, in which given accounts are detected either sockpuppet or non-sockpuppet. Sockpuppets has become significant issues, in which one can have fake identity for some specific purpose or malicious use. Text categorization is also performed with binary classification. This research synthesizes binary classification in which various approaches for binary classification are discussed.


  1. Thamar Solorio, Ragib Hasan and Mainul Mizan, "A Case Study of Sockpuppet Detection in Wikipedia", Proceedings of the Workshop on Language in Social Media(LASM 2013),Pages 59-68,Atlanta,Georgia,June 13 2013.@2013 Association for Computational Linguistics.
  2. Michail Tsikerdekis and Sherali Zeadally, "Multiple Account Identity Deception Detection in Social Media Using Non Verbal Behavior", IEEE Transactions on Information Forensics and Security, Vol 9, No 8, August 2014.
  3. Thamar Solorio, Ragib Hasan and Mainul Mizan, "Sockpuppet Detection in Wikipedia :A Corpus of Real-World Deceptive Writing For Linking Writing", arXiv:1310.6772v1[cs.CL] 24 Oct 2013.
  4. Xueling Zheng, Yiu Ming Lai, K.P. Chow, Lucas C.K. Hui and S.M. Yiu, "Detection of Sockpuppets in Online Discussion Forums", HKU CS Tech Report TR-2011-03.
  5. Sadia Afroz, Michael Brennan and Rachel Greenstadt, "Detecting Hoaxes Frauds and Deception in Writing Style Online". 2011.
  6. Dhanyasree P*, Sajitha Krishnan and Ambikadevi Amma T, "Deception Detection in Social Media through Combined Verbal and Non-Verbal Behavior ", International Journal of Advanced Research in Computer Science and Software Engineering , Volume 5, Issue 4, 2015.
  7. M Balaanand,R Soumipriya,S Sivaranjani and S Sankari, "Identifying Fake Users in Social Networks Using Non-Verbal Behaviour". International Journal of Technology and Engineering System (IJTES)Vol 7. No.2 2015 Pp. 157-161©gopalax Journals, Singapore.
  8. Sheetal Antony, Prof. B. S. Umashankar, "Identity Deception Detection and Security in Social Medium, IJCSMC, Vol. 5, Issue 4, April 2016, pg.499-502.
  9. Zaher Yamak, Julien Saunier and Laurent Vercouter, " Detection of Multiple Identity Manipulation in Collaborative Projects", IW3C2, WWW'16 Companion, April 11-15, 2016, Montreal, Quebec, Canada. ACM 978-1-4503-4144-8/04.
  10. Asaf Shabtai, Robert Moskovitch, Yuval Elovici and Chanan Glezer, " Detection of malicious code by applying machine learning classifiers on static features: A state -of-the-art-survey ", INFORMATION SECURITY TECHNICAL REPORT 14 (2009) 16-29, ELSEVIER.
  11. Antu Mary Kuruvilla1 and Saira Varghese2, "A Survey on detecting Identity Deception in Social Media Applications", International Journal of Modern Trends in Engineering and Research (IJMTER) Volume 02, Issue 04, [April – 2015] ISSN (Online):2349–9745 ; ISSN (Print):2393-8161.
  12. Ashkan Sami, B. Yadegari, N. Peiravian, and S. Hashemi and A. Hamze, "Malware detection based on mining API calls", SAC '10: Proceedings of the ACM Symposium on Applied Computing, pp. 1020-1025, 2010.
  13. G.Ganesh Sundarkumar and Vadlamani Ravi, "Malware Detection by Text and Data Mining".IEEE2013..
  14. Prasha Shrestha,Suraj Maharajan,Gabriela Ramirez de la Rosa,Alan Sprague,Thamar Solorio and Gracy Warner, "Using String Information for Malware Family Identification" @Springer International Publishing Switzerland 2014,A.L.C.Bazzan and K.Pichara(Eds.):IBERAMIA 2014,LNAI 8864,pp.686-697,2014.DOI:10.1007/978-3-319-12027-0_55
  15. Michael Bailey, Jon Oberheide, Z. Morley Mao, Farnam Jahanian and Jose Nazario, " Automated Classification and Analysis of Internet Malware". April 26 2007
  16. Gaston L’Huillier, Alejandro Hevia, Richard Weber and Sebastian Rios, "Latent Semantic Analysis and Keyword Extraction for Phishing Classification".IEEE2010.
  17. Rafiqul Islam, Ronghua Tian , Lynn M. Batten and Steve Versteeg," Classification of malware based on integrated static and dynamic features". Journal of Network and Computer Applications 36 (2013) 646–656. ELSEVIER.
  18. Ali Danesh, Behzad Moshiri and Omid Fatemi, "Improve Text Classification Accuracy based on Classifier Fusion Methods".2007 IEEE Xplore.
  19. Aytuğ Onana, Serdar Korukoğlub and Hasan Bulutb, " Ensemble of keyword extraction methods and classifiers in text classification". A. Onan et al. / Expert Systems With Applications 57 (2016) 232–247.
  20. Baoxun Xu, Xiufeng Guo, Yumming Ye and Jiefeng Cheng, "An Improved Random Forest Classifier for Text Categorization", [JOURNAL OF COMPUTERS] VOL. 7, NO. 12, DECEMBER 2012.
  21. M. Sivakumar, C. Karthika and P. Renuga, "A Hybrid Text Classification Approach using KNN and SVM", [IJIRSET] Volume 3, Special Issue 3, March 2014.
  22. Sundus Hassan, Muhammad Rafi and Muhammad Shahid Shaikh, "Comparing SVM and Naive Classifiers for Text categorization with Wikitology as knowledge enrichment". IEEE Xplore 2012.


Sockpuppets, Non sockpuppets, multiple identity deception, text categorization, NB, SVM, Random Forest, Ensemble methods and Binary Classification