A comparison on multi-class classification methods based on least squares twin support vector machine
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 trainingtesting 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. Friedmans statistic and Nemenyi post hoc tests are also used to test significance of predictive accuracy differences between classifiers.
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