ERROR-CORRECTING OUTPUT CODING FOR TEXT CLASSIFICATION
Armin Berger · 1999
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. 1 Introduction Error-correcting output coding is a recipe for solving multi-way classification problems. It works in two stages: first, independently construct many subordinate classifiers, each responsible for r...