Detecting fuzzy-rough conditional anomalies

Abstract

The purpose of conditional anomaly detection is to identify samples that significantly deviate from the majority of other samples under specific conditions within a dataset. It has been successfully applied to numerous practical scenarios such as forest fire prevention, gas well leakage detection, and remote sensing data analysis. Aiming at the issue of conditional anomaly detection, this paper utilizes the characteristics of fuzzy rough set theory to construct a conditional anomaly detection method that can effectively handle numerical or mixed attribute data. By defining the fuzzy inner boundary, the subset of contextual data is first divided into two parts, i.e. the fuzzy lower approximation and the fuzzy inner boundary. Subsequently, the fuzzy inner boundary is further divided into two distinct segments: the fuzzy abnormal boundary and the fuzzy main boundary. So far, three-way regions can be obtained, i.e., the fuzzy abnormal boundary, the fuzzy main boundary, and the fuzzy lower approximation. Then, a fuzzy-rough conditional anomaly detection model is constructed based on the above three-way regions. Finally, a related algorithm is proposed for the detection model and its effectiveness is verified by data experiments.