Second-Order Biased Random Walk Outlier Detection Based on Bi-Level Granular-Ball Knowledge Representation

Outlier detection is an effective technique for identifying abnormal samples in complex data. Random walks effectively detect outliers by analyzing graph transition patterns. However, existing methods only consider local transitions and fail to capture complex structural patterns in complex data. Granular-ball computing based outlier detection methods have better robustness and efficiency. Nevertheless, these methods use a coarse-granularity representation of granular-balls and ignore the large number of sample information within the granular-balls. To address the above issues, this paper develops a bi-level granular-ball based second-order biased random walk outlier detection method. First, a bi-level granular-ball knowledge representation method is proposed to address the information distortion inherent in granular-ball computing-based methods. Then, a granular-ball anomaly membership evaluation metric is introduced, which leverages second-order biased random walk, to endow granular-balls with coarse-granularity anomaly degrees. Subsequently, a bi-level granular-ball anomaly classifier is designed to map coarse-granularity granular-ball anomaly degrees to fine-granularity sample-level anomaly degrees. Finally, a distilled outlier factor is defined, which selects optimal attribute sequences through granular-ball construction on attributes, for outlier detection. At the same time, a corresponding outlier detection algorithm is proposed. Experiments on datasets are conducted to compare the proposed algorithm with six other algorithms. The experimental results show that the algorithm has better performance and a certain degree of robustness.