Extensions to Online Feature Selection Using Bagging and Boosting.

Abstract

Feature subset selection can be used to sieve through large volumes of data and discover the most informative subset of variables for a particular learning problem. Yet, due to memory and other resource constraints (e.g., CPU availability), many of the state-of-the-art feature subset selection methods cannot be extended to high dimensional data, or data sets with an extremely large volume of instances. In this brief, we extend online feature selection (OFS), a recently introduced approach that uses partial feature information, by developing an ensemble of online linear models to make predictions. The OFS approach employs a linear model as the base classifier, which allows the $l_{0}$ -norm of the parameter vector to be constrained to perform feature selection leading to sparse linear models. We demonstrate that the proposed ensemble model typically yields a smaller error rate than any single linear model, while maintaining the same level of sparsity and complexity at the time of testing.