Outlier Detection Algorithms for Open Environments

Abstract

The high dimensionality and massive amount of data in open environments make the existing low-dimensional outlier detection methods time-consuming. The support vector machine (SVM) is a commonly used outlier detection method. However, the SVM still faces the problem of difficulty in obtaining optimal parameters quickly and effectively, resulting in low detection efficiency, poor stability, and difficulty in applying to open environment datasets. In order to improve the efficiency and stability of outlier detection, this paper proposes an improved sparrow search algorithm and uses it to optimize SVM parameters. First, the traditional sparrow search algorithm is improved by using improved backtracking learning and variable logarithmic spirals. Then, the improved sparrow search method is used to optimize SVM parameters, and the optimized support vector machine is applied to the field of outlier detection. Simulation experimental results show that the proposed method is significantly better than the compared classification algorithms in multiple evaluation indicators, with better detection efficiency, stability, and generalization ability.