Error Correction Output Codes: Overview, Challenges and Future Trends

Abstract

One of the most effective way to address multiclass classification problem is to use a set of judiciously designed binary classifiers and to carefully combine their results. Error-Correcting Output Codes (ECOC) is one of the successful frameworks that allows a division of labour through multiple binary classifications. This paper provides a brief introduction to the state-of-theart ECOC types, various decoding methods that merges binary classification results, and a comparative study that lays out challenges, advantages and disadvantages. We also provide few important applications of ECOC, its performance on MNIST data set and some of the future trends.