2015 AComparisononMultiClassClassifi

• (Tomar & Agarwal, 2015) ⇒ Divya Tomar, and Sonali Agarwal. (2015). “A Comparison on Multi-class Classification Methods based on Least Squares Twin Support Vector Machine.” In: Knowledge-Based Systems Journal, 81(C). doi:10.1016/j.knosys.2015.02.009

Subject Headings: SVM Algorithm.

Cited By

• http://scholar.google.com/scholar?q=%222015%22+A+Comparison+on+Multi-class+Classification+Methods+based+on+Least+Squares+Twin+Support+Vector+Machine
• http://dl.acm.org/citation.cfm?id=2781905.2782263&preflayout=flat#citedby

Quotes

Abstract

Least Squares Twin Support Vector Machine (LSTSVM) is a binary classifier and the extension of it to multiclass is still an ongoing research issue. In this paper, we extended the formulation of binary LSTSVM classifier to multi-class by using the concepts such as “One-versus-All", “One-versus-One", “All-versus-One” and Directed Acyclic Graph (DAG). This paper performs a comparative analysis of these multi-classifiers in terms of their advantages, disadvantages and computational complexity. The performance of all the four proposed classifiers has been validated on twelve benchmark datasets by using predictive accuracy and training-testing time. All the proposed multi-classifiers have shown better performance as compared to the typical multi-classifiers based on 'Support Vector Machine' and 'Twin Support Vector Machine'. Friedman's statistic and Nemenyi post hoc tests are also used to test significance of predictive accuracy differences between classifiers.

…

…

References

;

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 AComparisononMultiClassClassifi	Divya Tomar Sonali Agarwal			A Comparison on Multi-class Classification Methods based on Least Squares Twin Support Vector Machine				10.1016/j.knosys.2015.02.009		2015