Heuristic regression function estimation methods for data streams with concept drift

Abstract

In this paper the regression function methods based on Parzen kernels are investigated. Both the modeled function and the variance of noise are assumed to be time-varying. The commonly known kernel estimator is extended by adopting two popular tools often applied in concept drifting data stream scenario. The first tool is a sliding window, in which only a constant number of recently received data elements affects the estimator. The second one is the forgetting factor. In this case at each time step past data become less and less important. These heuristic approaches are experimentally compared with the basic mathematically justified estimator and demonstrate similar accuracy.