Error-Correcting Output Coding for Text Classification

This paper applies error-correcting output coding (ECOC) to the task of document categorization. ECOC, of recent vintage in the AI literature, is a method for decomposing a multiway classification problem into many binary classification tasks, and then combining the results of the subtasks into a hypothesized solution to the original problem. There has been much recent interest in the machine learning community about algorithms which integrate “advice” from many subordinate predictors into a single classifier, and error-correcting output coding is one such technique. We provide experimental results on several real-world datasets, extracted from the Internet, which demonstrate that ECOC can offer significant improvements in accuracy over conventional classification algorithms.