Abstract: Support vector machine (SVM) is a common method for outlier detection. However, there are still common problems that it is difficult to quickly and effectively obtain the optimal paramet- ers, resulting in low detection efficiency and poor stability. In view of this, an improved sparrow search algorithm is proposed to optimize the parameters of SVM. Firstly, the traditional sparrow search algorithm is improved by improved refraction reverse learning and variable logarithm spiral. Then, the improved sparrow search algorithm (ISSA) is used to optimize the parameters of SVM. Finally, the optimized SVM is used in the field of outlier detection. The simulation results show that, under the two evaluation indexes of G-mean and F-measure, the optimized SVM is obviously better than the other three classification algorithms, and has better detection efficiency, stability, and generalization ability. 

Key words: sparrow search algorithm, support vector machine, refraction reverse learning, variable logarithmic spiral, outlier detection