基于欠采样的零阶优化算法

非平衡学习吸引了许多研究者的关注。一般情况下,少数类是更值得关注的,并且其误分类代价要远高于多数类。由于非平衡数据分布的非均衡性,标准的分类算法将难以适用。为了解决非平衡数据分类问题,给出了基于欠采样的零阶优化算法。首先,为了降低数据非平衡分布的影响,针对不同非平衡比的数据集给出了不同的两种采样策略。然后,采用了一种引入间隔均值项的支持向量机(Support vector machine,SVM)优化模型进行分类,并使用带有方差减小的零阶随机梯度下降算法进行求解,提高了算法的精度。在非平衡数据上进行了对比实验,实验证明提出的方法有效提高了非平衡数据的分类效果。

Zeroth Order Optimization Algorithm Based on Undersampling

In recent years, imbalanced learning has attracted the attention of many researchers. In general, minority classes are more noteworthy, and the cost of misclassification is much higher than that of majority classes. Because of the imbalanced distribution of imbalanced data, the standard classification algorithms will be difficult to apply. In order to solve the problem of imbalanced data classification, a zeroth-order optimization algorithm based on under-sampling is presented. Firstly, in order to reduce the influence of imbalanced data distribution, two different sampling strategies are adopted for data sets with different imbalanced ratios. Then, an SVM(Support vector machine) model with margin mean term is used for classification, and a zeroth-order stochastic gradient descent algorithm with reduced variance is used to solve the problem. At the same time, the accuracy of the algorithm is improved. A comparative experiment is carried out on imbalanced data, and the experimental results show that the proposed method effectively improves the classification effect of imbalanced data.