Discriminative Category Matching: Efficient Text Classification for Huge Document Collections Gabriel Pui Cheong Fung, Jeffrey Xu Yu, and Hongjun Lu   View Paper (PDF)   Return to Main-Track Regular Papers Abstract With the rapid growth of textual information available on the Internet, having a good model for classifying and managing documents automatically is undoubtly important. When more documents are archived, new terms, new concepts and concept-drift will frequently appear. Without a doubt, updating the classification model frequently rather than using the old model for a very long period is absolutely essential. Here, the challenges are: a) obtain a high accuracy classification model; b) consume low computational time for both model training and operation; and c) occupy low storage space. However, none of the existing classification approaches could achieve all of these requirements. In this paper, we propose a novel text classification approach, called Discriminative Category Matching, which could achieve all of the stated characteristics. Extensive experiments using two benchmarks and a large real-life collection are conducted. The encouraging results indicated that our approach is hignhly feasible. DiSC'03 © 2003 Association for Computing Machinery