A comparison of classifiers and document representations for the routing problem

Abstract

In this paper, we compare learning techniques based on statistical classification to traditional methods of relevance feedback for the document routing problem. We consider three classification techniques which have decision rules that are derived via explicit error minimization linear discriminant analysis, logistic regression, and neuraf networks. We demonstrate that the classifiers perform 10-15% better than relevance feedback via Rocchio expansion for the TREC-2 and TREC-3 routing tasks.