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We present an efficient and effective method which extends the Boosting family of classifiers to allow the weighted classes. Typically classifiers do not treat individual classes separately. For most real world applications, this is not the case, not all classes have the same importance. The accuracy of a particular class can be more critical than others. In this paper we extend the mathematical formulation for Boosting to weigh the classes differently during training. We have evaluated this method for call classification in AT&T spoken language understanding system. Our results indicate significant improvements in the "important" classes without a significant loss in the overall performance.