Iterative Subset Selection for Feature Drifting Data Streams

Feature selection has been studied and shown to improve classifier performance in standard batch data mining but is mostly unexplored in data stream mining. Feature selection becomes even more important when the relevant subset of features changes over time, as the underlying concept of a data stream drifts. This specific kind of drift is known as feature drift and requires specific techniques not only to determine which features are the most important but also to take advantage of them. This paper presents a novel method of feature subset selection specialized for dealing with the occurrence of feature drifts called Iterative Subset Selection (ISS), which splits the feature selection process into two stages by first ranking the features, and then iteratively selecting features from the ranking. Applying our feature selection method together with Naive Bayes or k-Nearest Neighbour as a classifier, results in compelling accuracy improvements, compared to prior work.