An avalanche of data available in the stream form is overstretching our data analyzing ability. In this paper, we propose a novel load shedding method that enables fast and accurate stream data classification. We transform input data so that its class information concentrates on a few features, and we introduce a progressive classifier that makes prediction with partial input. We take advantage of stream data's temporal locality -for example, readings from a temperature sensor usually do not change dramatically over a short period of time -for load shedding. We first show that temporal locality of the original data is preserved by our transform, then we utilize positive and negative knowledge about the data (which is of much smaller size than the data itself) for classification. We employ both analytical and empirical analysis to demonstrate the advantage of our approach.