A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is
ESANN 2021 proceedings
The notion of concept drift refers to the phenomenon that the data distribution changes over time. If drift occurs, machine learning models need adjustment. Since drift can be inhomogeneous, suitable actions depending on the location in data space. In this paper we address the challenge to partition the data space into segments with homogeneous drift characteristics. We formalize this objective as an independence criterion, and derive a robust and efficient training algorithm based thereon. Wedoi:10.14428/esann/2021.es2021-93 fatcat:kyfoklxgxbckjbclnyiyhjvsfe