基于差分孪生卷积神经网络的大规模不平衡数据分类算法

CLASSIFICATION ALGORITHM OF BIG SCALE IMBALANCED DATA BASED ON THE DIFFERENTIAL SIAMESE CONVOLUTION NEURAL NETWORKS

传统基于支持向量机的不平衡数据分类算法包含矩阵运算,无法应用于大规模的不平衡数据集.针对这种情况,提出基于差分孪生卷积神经网络的大规模不平衡数据分类算法.设计差分卷积机制增强卷积神经网络的深度结构表示能力,在不改变滤波器数量的情况下提高模型的判别能力.通过差分孪生卷积神经网络分别优化每个类的特征图,每个类关联多个超平面,根据输入样本与超平面的距离决定输出样本的类标签.基于多组不平衡数据集的实验结果表明,该算法实现了较好的分类性能.

The traditional classification algorithms for imbalanced data based on the support vector machine include matrix operations,which cannot be applied in big scale imbalanced datasets.In view of this,we propose a classification algorithm for big scale imbalanced data based on the differential Siamese convolution neural networks.We designed a differential convolution mechanism to enhance the deep structure representation ability of convolution neural networks,so that it can improve the discriminative ability of models without changing the number of filters.The Siamese mechanism was introduced to convolution neural networks.The feature maps of each class were optimized by differential convolution Siamese networks.Each class was associated with several hyperplanes,and the model decided the class labels of output samples according to the distances between the input samples and hyperplanes.Experimental results based on several imbalanced datasets show that the proposed algorithm performs better classification results,and has faster speed.