A Hybrid Clustering Approach Based on Fuzzy Logic and Evolutionary Computation for Anomaly Detection

Abstract

In this study, a new approach for novelty and anomaly detection, called HPFuzzNDA, is introduced. It is similar to the Possibilistic Fuzzy multi-class Novelty Detector (PFuzzND), which was originally developed for data streams. Both algorithms initially use a portion of labelled data from known classes to divide them into a given number of clusters, and then attempt to determine if the new instances, which may be unlabelled, belong to the known or novel classes or if they are anomalies, namely if they are extreme values that deviate from other observations, indicating noise or errors in measurement. However, for each class in HPFuzzNDA clusters are designed by using the new evolutionary algorithm NL-SHADE-RSP, the latter is a modification of the well-known L-SHADE approach. Additionally, the number of clusters for all classes is automatically adjusted in each step of HPFuzzNDA to improve its efficiency. The performance of the HPFuzzNDA approach was evaluated on a set of benchmark problems, specifically generated for novelty and anomaly detection. Experimental results demonstrated the workability and usefulness of the proposed approach as it was able to detect extensions of the known classes and to find new classes in addition to the anomalies. Moreover, numerical results showed that it outperformed PFuzzND. This was exhibited by the new mechanism proposed for cluster adjustments allowing HPFuzzNDA to achieve better classification accuracy in addition to better results in terms of macro F-score metric.