Active Incremental Feature Selection Using a Fuzzy-Rough-Set-Based Information Entropy

Abstract

Feature selection is a popular technique of preprocessing data. In order to deal with dynamic or large data, incremental feature selection has been developed, in which the features selected from existing data are integrated with those mined from both existing and dynamic data in the manner of incremental computation. Fuzzy rough set theory is powerful in handling uncertainty in real-valued data or even mixed data, and one of its most important applications is feature selection. Nevertheless, not much work has been found on fuzzy-rough-set-based incremental feature selection. Therefore, in this article, we investigate the incremental feature selection using a fuzzy-rough-set-based information entropy with incoming instances. Specifically, the representative instances from the incoming ones are first selected according to the information coverage of fuzzy granules generated by fuzzy rough sets. Then, the incremental mechanism of the fuzzy-rough-set-based information entropy is formulated by adding newcome instances. Finally, an incremental feature selection procedure, which we call active incremental feature selection, is proposed. Furthermore, some numerical experiments are conducted to assess the performance of the proposed feature selection algorithm, and the results show that our algorithm is of a prominent advantage in terms of computational time, especially for a dataset with large number of instances.