Dataset condensation using OWA fuzzy-rough set-based nearest neighbor classifier

The application of fuzzy-rough sets for the task of feature selection and rule induction has been the topic of much interest recently. However, applications for data instance or object selection have attracted much less attention. In this paper a novel approach for dataset condensation based on ordered weighted aggregation (OWA) fuzzy-rough sets is proposed in the context of the KNN classifier. Initially, a rank is assigned to each data instance of the dataset, based upon a novel measure inspired by fuzzy-rough sets. High quality data instances which possess higher ranks are retained based on another new metric whilst others can then be removed. An additional novel innovation is the elimination of any subjective user-specified threshold in order to determine which particular data instances are candidates for removal. This is in keeping with the rough set ideology of data-driven approaches. A series of non-parametric statistical tests demonstrate that the technique is very effective and can produce useful condensations of the data.