Classifiers that refrain from classification in certain cases can significantly reduce the misclassification cost. However, the parameters for such abstaining classifiers are often set in a rather ad-hoc manner. We propose a method to optimally build a specific type of abstaining binary classifiers using ROC analysis. These classifiers are built based on optimization criteria in the following three models: cost-based, bounded-abstention and bounded-improvement. We demonstrate the usage and applications of these models to effectively reduce misclassification cost in real classification systems. The method has been validated with a ROC building algorithm and cross-validation on 15 UCI KDD datasets.

Floating car data (FCD), i.e. traffic and in-car data collected and transmitted by moving cars, is an emerging telematics application providing a range of networked services to road users. FCD is an example of a large sensor network, whose complexity increases with the number of session participants. Our work examines how deploying application-specific functions in the network can help in scaling such systems. We have chosen to configure these functions on a dedicated device that is commonly called a middlebox. These middleboxes use programmable network processors in order to attain the required processing and forwarding speeds, while communicating with each other and a back-end server using standard middleware components. We present our experience in using such a programmable middlebox in the scaling of a large telematics service, explaining how the middlebox fits into the end-to-end application.