A Survey on Feature Drift Adaptation

Mining data streams is of the utmost importance due to its appearance in many real-world situations, such as: sensor networks, stock market analysis and computer networks intrusion detection systems. Data streams are, by definition, potentially unbounded sequences of data that arrive intermittently at rapid rates. Extracting useful knowledge from data streams embeds virtually all problems from conventional data mining with the addition of single-pass real-time processing within limited time and memory space. Additionally, due to its ephemeral nature, it is expected that streams undergo changes in its data distribution denominated concept drifts. In this work, we focus on one specific kind of concept drift that has not been extensively addressed in the literature, namely feature drift. A feature drift happens when changes occur in the set of features, such that a subset of features become, or cease to be, relevant to the learning problem. Specifically, changes in the relevance of features directly imply modifications in the decision boundary to be learned, thus the learner must detect and adapt to according to it. Timely detection and recover from feature drifts is a challenging task that can be modeled after a dynamic feature selection problem. In this paper we survey existing work on dynamic feature selection for data streams that acts either implicitly or explicitly. We conclude that there is a need for future research in this area, which we highlight as future research directions.

I. Introduction

In the last decades the interest in mining massive and potentially unbounded datasets which arrive at rapid rates, namely data streams, has grown substantially. Mining data streams is of the utmost importance since many available data generators produce enormous amounts of data over time. Examples of data streams include sensor networks, wearable sensors, computer network traffic sniffers and video surveillance, to name a few. Aiming at extracting useful knowledge from these massive amounts of data, a variety of inductive learning techniques were developed and achieved concrete results in both supervised [1], [2] and unsupervised [3]–[6] tasks.