Combining Subclassifiers in Text Categorization: A DST-Based Solution and a Case Study

Abstract

Text categorization systems often use machine learning techniques to induce document classifiers from preclassified examples. The fact that each example document belongs to many classes often leads to very high computational costs that sometimes grow exponentially in the number of features. Seeking to reduce these costs, we explored the possibility of running a "baseline induction algorithm" separately for subsets of features, obtaining a set of classifiers to be combined. For the specific case of classifiers that return not only class labels but also confidences in these labels, we investigate here a few alternative fusion techniques, including our own mechanism that was inspired by the Dempster-Shafer Theory. The paper describes the algorithm and, in our specific case study, compares its performance to that of more traditional mechanisms.