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Machine Learning: A Review on Binary Classification

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Roshan Kumari, Saurabh Kr. Srivastava
10.5120/ijca2017913083

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

@article{10.5120/ijca2017913083,
	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 = {http://www.ijcaonline.org/archives/volume160/number7/27084-2017913083},
	doi = {10.5120/ijca2017913083},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

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.

References

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Keywords

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