10. A method of classifying a document comprising: using a processor (or computer) to perform the steps of: classifying said document using output from one or more of said classifier engines based on a comparison of one or more metrics for each classifier engine; using a set of training documents to rank said classifier engines from best to worst; generating, for each classifier engine, a list of probabilities of said document being classified by each classifier engine into each one of a group of possible classes; determining if the best classifier engine has a highest probability that exceeds a predetermined threshold; if so, using output from said best classifier engine to classify said document; and if not, summing said probabilities for each class, and classifying said document into the class with the largest sum of probabilities.
10. A method of classifying a document comprising: using a processor (or computer) to perform the steps of: classifying said document using output from one or more of said classifier engines based on a comparison of one or more metrics for each classifier engine; using a set of training documents to rank said classifier engines from best to worst; generating, for each classifier engine, a list of probabilities of said document being classified by each classifier engine into each one of a group of possible classes; determining if the best classifier engine has a highest probability that exceeds a predetermined threshold; if so, using output from said best classifier engine to classify said document; and if not, summing said probabilities for each class, and classifying said document into the class with the largest sum of probabilities. 12. The method of claim 10 wherein said classifier engines are ranked from best to worst based on engine accuracy.