Feature subset selection for data and feature streams: a review

Abstract

Real-world problems are commonly characterized by a high feature dimensionality, which hinders the modelling and descriptive analysis of the data. However, some of these data may be irrelevant or redundant for the learning process. Different approaches can be used to reduce this information, improving not only the speed of building models but also their performance and interpretability. In this review, we focus on feature subset selection (FSS) techniques, which select a subset of the original feature set without making any transformation on the attributes. Traditional batch FSS algorithms may not be adequate to efficiently handle large volumes of data, either because memory problems arise or data are received in a sequential manner. Thus, this article aims to survey the state of the art of incremental FSS algorithms, which can perform more efficiently under these circumstances. Different strategies are described, such as incrementally updating feature weights, applying information theory or using rough set-based FSS, as well as multiple supervised and unsupervised learning tasks where the application of FSS is interesting.