Fuzzy rough feature selection based on OWA aggregation of fuzzy relations

The interaction between features, or attributes, of a dataset forms a major topic in machine learning and data mining. In particular, a wide range of methods have been established for feature selection, ranking, and grouping. Amongst these, fuzzy rough set based feature selection (FRFS) has been shown to be highly effective at reducing dimensionality for real-valued datasets while retaining attribute semantics. In fuzzy rough sets, the concept of crisp equivalence classes is extended by fuzzy similarity relations, and real-valued similarity measures can be captured between data instances in terms of their attribute values. Therefore, it is desirable to study the aggregation of fuzzy similarity relations to reflect the interactions between attributes. This paper presents an approach that employs OWA aggregation of fuzzy similarity relations to better perform FRFS. A high degree of modelling flexibility is provided by choosing the stress function in OWA. Experimental studies demonstrate that through using different stress functions, different features may be selected; and that given an appropriate stress function, the quality of selected features can improve over that achievable by the state-of-the-art FRFS, in performing classification tasks.