CPF: Concept Profiling Framework for Recurring Drifts in Data Streams

Abstract

We propose the Concept Profiling Framework (CPF), a meta-learner that uses a concept drift detector and a collection of classification models to perform effective classification on data streams with recurrent concept drifts, through relating models by similarity of their classifying behaviour. We introduce a memory-efficient version of our framework and show that it can operate faster and with less memory than a naïve implementation while achieving similar accuracy. We compare this memory-efficient version of CPF to a state-of-the-art meta-learner made to handle recurrent drift and show that we can regularly achieve improved classification accuracy along with runtime and memory use. We provide results from testing on synthetic and real-world datasets to prove CPF’s value in classifying data streams with recurrent concepts.