Empirical Study On Error Correcting Output Code Based On Multiclass

A common way to address a multi-class classification problem is to design a model that consists of hand picked binary classifiers and to combine them so as to solve the problem. Error such framework that deals with multi-class classification problems. Recent works in the ECOC domain has shown promising results demonstrating improved performance. Therefore, ECOC framework is a powerful tool to deal with multi-class classification problems. The the base classifiers. This paper introduces state sparse random, DECOC, forest-ECOC, and ECOC hamming, laplacian, β-density, attenuated, loss along with empirical study of ECOC following comparison of various ECOC methods in the above context. Towards the end, our paper consolidates details relating to comparison of various classification methods with Error Correcting Output Code method available in supplement to our studies.