1. A method of generating and using classifier models in a mobile device based on application types having distinct characteristics, comprising: receiving, in a processor of the mobile device, a finite state machine that includes information that is suitable for conversion into a plurality of boosted decision stumps, wherein each of the plurality of boosted decision stumps evaluate one of a plurality of test conditions; converting the information included in the received finite state machine into the plurality of boosted decision stumps that each evaluate one of the plurality of test conditions; generating a family of lean classifier models in the mobile device based on the plurality of boosted decision stumps; generating, via the processor of the mobile device, an application-type-specific classifier model that includes and prioritizes boosted decision stumps in the plurality of boosted decision stumps that evaluate a subset of test conditions in the plurality of test conditions, wherein the subset of test conditions are determined to evaluate mobile device features that are used by one type of software application, wherein the one type of software application is determined to be suitable for executing on the mobile device; selecting a lean classifier model from the family of lean classifier models; and applying collected behavior information to the generated application-type-specific classifier model and the selected lean classifier model in parallel to classify a behavior of the mobile device.