Analyzing the Impact of Feature Drifts in Streaming Learning

Abstract

Learning from data streams requires efficient algorithms capable of deriving a model accordingly to the arrival of new instances. Data streams are by definition unbounded sequences of data that are possibly non stationary, i.e. they may undergo changes in data distribution, phenomenon named concept drift. Concept drifts force streaming learning algorithms to detect and adapt to such changes in order to present feasible accuracy throughout time. Nonetheless, most of works presented in the literature do not account for a specific kind of drifts: feature drifts. Feature drifts occur whenever the relevance of an arbitrary attribute changes through time, also impacting the concept to be learned. In this paper we (i) verify the occurrence of feature drift in a publicly available dataset, (ii) present a synthetic data stream generator capable of performing feature drifts and (iii) analyze the impact of this type of drift in stream learning algorithms, enlightening that there is room and the need for dynamic feature selection strategies for data streams.