Feature Selection for Concept Drift Detection

Abstract

Feature selection is one of the most relevant preprocessing and analysis techniques in machine learning.It can dramatically increase the performance of learning algorithms and also provide relevant information on the data.In online and stream learning concept drift, i.e., the change of the underlying distribution over time, can cause tremendous problems for learning models and data analysis.While there do exist feature selection methods for online learning, to the best of our knowledge there do not exist methods to perform feature selection for drift detection, i.e., to increase the performance of drift detectors and to analyze the drift itself.In this work, we study feature selection for concept drift detection and provide a formal derivation and semantic interpretation thereof.We empirically show the relevance of our considerations on several benchmarks.