Concept Drift Adaptation with Incremental–Decremental SVM

Abstract

Data classification in streams where the underlying distribution changes over time is known to be difficult. This problem—known as concept drift detection—involves two aspects: (i) detecting the concept drift and (ii) adapting the classifier. Online training only considers the most recent samples; they form the so-called shifting window. Dynamic adaptation to concept drift is performed by varying the width of the window. Defining an online Support Vector Machine (SVM) classifier able to cope with concept drift by dynamically changing the window size and avoiding retraining from scratch is currently an open problem. We introduce the Adaptive Incremental–Decremental SVM (AIDSVM), a model that adjusts the shifting window width using the Hoeffding statistical test. We evaluate AIDSVM performance on both synthetic and real-world drift datasets. Experiments show a significant accuracy improvement when encountering concept drift, compared with similar drift detection models defined in the literature. The AIDSVM is efficient, since it is not retrained from scratch after the shifting window slides.