Comparative study of neighbor-based methods for local outlier detection

Abstract

The neighbor-based method has become a powerful tool for addressing the outlier detection problem, which aims to assess the abnormality of a sample based on its compactness relative to neighboring samples. However, most existing methods primarily focus on designing various processes to identify outliers, while the contributions of different types of neighbors to the detection process have not been adequately explored. To address this gap, this article investigates the role of neighbors in existing outlier detection algorithms and introduces a taxonomy that utilizes three key components: information, neighbor, and methodology, to define hybrid methods. This taxonomy provides a framework that can inspire the development of novel neighbor-based outlier detection algorithms by combining different components from each level. Extensive comparative experiments on both synthetic and real-world datasets, including performance evaluations and case studies, demonstrate that reverse K-nearest neighbor-based methods perform well and that dynamic selection methods are particularly effective in high-dimensional spaces. Furthermore, the results confirm that strategically selecting components from this taxonomy can lead to the development of algorithms that outperform existing methods.