Prototype-based fuzzy rough sets for outlier detection

Abstract

Outlier detection is a crucial task for ensuring the reliability of data analysis, aiming at identifying objects that markedly differ from typical patterns in the dataset. Recently, many methods based on fuzzy rough sets have shown promising performance. However, these methods overlook the influence of redundant attributes and include the relationships between outliers in the analysis. Targeting these important problems, we propose a novel approach called Prototype-based Fuzzy Rough Sets (PFRS), which performs outlier detection via prototype learning based on the selection of separable attributes. Specifically, information entropy is applied to select significant attributes, enhancing the separability of the feature space. More importantly, by employing prototype learning to acquire representative objects, PFRS effectively eliminates the impact of relationships among outliers. In addition, the fuzzy similarity between prototypes and objects is evaluated to reconstruct the traditional fuzzy upper and lower approximations. Finally, more reliable outlier scores are derived from PFRS. Extensive experiments using widely adopted algorithms verify the effectiveness of PFRS, confirming its outstanding performance.