Error-correcting Output Coding for Text Classiication

This paper applies error-correcting output coding (ECOC) to the task of document cate-gorization. ECOC, of recent vintage in the AI literature, is a method for decomposing a multiway classiication problem into many binary classiication 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 predic-tors into a single classiier, 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 ooer signiicant improvements in accuracy over conventional classiication algorithms.