Incremental feature selection with fuzzy rough sets for dynamic data sets

Abstract

Incremental mechanisms for feature selection in fuzzy rough sets are widely studied because they can effectively and efficiently complete feature selection tasks for dynamic data sets. However, the existing algorithms mainly focus on incremental mechanisms for dynamic data sets by increasing single dimensions of samples or features. In this study, we propose an incremental mechanism for feature selection with fuzzy rough sets where samples and features are added to dynamic data simultaneously. A relative discernibility relation is proposed to represent the samples and features of fuzzy rough sets in a unified manner. This relation provides a theoretical basis for the incremental analysis of dynamic data while simultaneously increasing the samples and features. By analyzing the changes in the relative discernibility relationship caused by the variations in samples and features, the feature and sample incremental mechanisms can be integrated well to form a unified method for simultaneously increasing the samples and features. Finally, an incremental algorithm is proposed for dynamic data sets based on the unified incremental process. The results obtained in numerical experiments showed that the algorithm can effectively deal with incremental feature selection by simultaneously adding features and samples.