A Survey paper on error correcting output code base on multiclass classification

Abstract

Error correcting output codes (ECOC) represent a successful extension of binary classifiers to address the multiclass problem. 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 .Recent works in the ECOC domain has shown promising results demonstrating improved performance. In this survey paper, we present an open source Error Error-Correcting Correcting Output Codes (ECOC) library. The ECOC framework is a powerful tool to deal with multiclass categorization problems. This library contains both state-of-the-art coding (one-versus-one, one, one one-versus-all, all, dense random, sparse random, DECOC, forest-ECOC, and ECOC-ONE) ONE) and decoding designs (hamming, Euclidean, inverse hamming, laplacian, ?-density, density, attenuated, loss-based, probabilistic kernel-based, based, and loss loss-weighted) with the parameters defined by the authors, as well as the option to include your own coding, decoding, and base classifier.