关键词: 非平衡数据, 垃圾文本识别, 否定选择密度聚类, 基于否定选择密度聚类的下采样算法（NSDC-DS）, 基于主成分分析的随机梯度下降（PCA-SGD）算法

Abstract:

In order to solve the problem of biased estimation of random samples, when using random under-sampling to improve the classifier performance for unbalanced communication samples, a Down-Sampling algorithm based on Negative Selection Density Clustering（NSDC-DS） is proposed. Firstly, the autogenous anomaly detection mechanism of negative selection algorithm is used to improve the traditional clustering, and the two are matched abnormally. The sampled communication samples are recognized with the NBSVM classifier. Then the negative selection clustering algorithm is used to evaluate the similarity of samples and improve the traditional down-sampling method. Finally, PCA is used to evaluate the information content of samples, and an improved PCA-SGD algorithm is proposed to tune model parameters and complete the semi-supervised recognition task of communication spam text. In order to verify the superiority of the improved algorithm, multiple data sets such as unbalanced communication text are used to compare and analyze the negative selection cluster, NSBC-US, PCA-SGD and the traditional model. Experimental results show that the improved model not only has good communication spam text recognition ability, but also has fast and stable convergence speed.

Key words: unbalanced data, spam text recognition, negative selection density clustering, Down-Sampling algorithm based on Negative Selection Density Clustering（NSDC-DS）, Stochastic Gradient Descent based on Principal Component Analysis（PCA-SGD） algorithm