Scaling Up Text Classification for Large File Systems

摘要

We combine the speed and scalability of infonnation retrieval with the generally superior classification accuracy offered by machine learning, yielding a two-phase text classifier that can scale to very large document corpora. We investigate the effect of different methods of formulating the query from the training set, as well as varying the query size. In empirical tests on the Reuters RCV1 corpus of 806,000 documents, we find runtime was easily reduced by a factor of 27x, with a somewhat surprising gain in F-measure compared with traditional text classification.