Self-adaptive weighted interaction feature selection based on robust fuzzy dominance rough sets for monotonic classification

Abstract

Feature selection is an effective dimensionality reduction technique for classification tasks. Monotonic classification task (MCT) is a special classification task in which features and decision obey a monotonic constraint, which is widely used in various application fields. Most of the existing feature selection methods for MCTs are based on some dominance rough set models. However, these methods only consider the correlation between features and decisions, and ignore the interaction between features. Moreover, these dominance rough set models are not robust to noisy information. Thus, to make up for these two deficiencies, we propose a novel feature selection approach based on an improved robust fuzzy dominance rough set for MCTs. First, a β−precision fuzzy dominance neighborhood rough set (β−PFDNRS) with robustness is proposed. Second, a feature evaluation index is presented, which not only considers the correlation between features and decision, but also considers the interaction between features. Finally, a β−PFDNRS based self-adaptive weighted interaction feature selection (β−SWIFS) algorithm is designed to select a feature sequence. Extensive experiments are conducted on fifteen public datasets, and the results show that the β−PFDNRS model has good robustness and the proposed feature selection algorithm has an excellent classification performance.