面向不平衡数据的分类算法

不平衡数据在分类时往往会偏向"多数",传统过采样生成的样本不能较好的表达原始数据集分布特征.改进的变分自编码器结合数据预处理方法,通过少数类样本训练,使用变分自编码器的生成器生成样本,用于以均衡训练数据集,从而解决传统采样导致的不平衡数据引起分类过拟合问题.我们在UCI四个常用的数据集上进行了实验,结果表明该算法在保证准确率的同时提高了F_measure和G_mean.

Classification Algorithm for Imbalanced Data Set

Imbalanced dataset tends to be biased towards "majority" when classifying, and samples generated by traditional over-sampling cannot well express the distribution characteristics of the original dataset. The improved variational autoencoders combine with data preprocessing method, generate samples by the generator of variational autoencoders trained by the minority class samples to balance the training data set, solve the overfitting problem caused by imbalanced dataset of traditional sampling. Experiments are carried out on four commonly used UCI datasets, the results demonstrate that the proposed method shows better classification performance in F_measure and G_mean with high accuracy.