针对不平衡数据集中少数类样本分类识别率较低的问题，提出一种基于代价敏感卷积神经网络（cost sensitive convolutional neural network，CSCNN）和AdaBoost的分类算法（classification algorithm based on cost sensitive convolutional neural network and AdaBoost，AdaBoost-CSCNN）。设置特定的代价敏感指标来协同卷积神经网络的交叉熵损失函数，从而构建CSCNN。在训练过程中，借助代价赋权机制降低少数类样本关键特征属性的损失度，实现单个CSCNN作为基分类器在AdaBoost中的分类效果。为验证算法的有效性，使用Accuracy、Recall、F1值和AUC这4个评价指标在9个具有不同不平衡率的数据集上开展实验。结果表明，AdaBoost-CSCNN算法处理不平衡数据集分类问题有较好的显示度。
Aiming at the problem of low recognition rate of a few types of samples in unbalanced data sets, a classification algorithm based on cost sensitive convolutional neural network and AdaBoost (AdaBoost-CSCNN) was proposed. The cost sensitive convolutional neural network (CSCNN) is constructed by coordinating the cross entropy loss function of convolutional neural network (CNN) with a specific cost sensitive index. In training process, cost weighting mechanism is used to reduce the loss degree of key feature attributes of a few samples and realize the classification effect of a single CSCNN as a base classifier in AdaBoost. To verify the effectiveness of the algorithm, we carried out experiments on 9 data sets with different imbalance rates. Experimental performances, including Accuracy, Recall, F1-score and AUC, show that the AdaBoost-CSCNN algorithm has a good display for unbalanced data set classification.
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