Category imbalance is one of the most popular and important issues in the domain of classification. Emotion classification model trained on imbalanced datasets easily leads to unreliable prediction. The traditional machine learning method tends to favor the majority class, which leads to the lack of minority class information in the model. Moreover, most existing models will produce abnormal sensitivity issues or performance degradation. We propose a robust learning algorithm based on adaptive cost-sensitiveity and recursive denoising, which is a generalized framework and can be incorporated into most stochastic optimization algorithms. The proposed method uses the dynamic kernel distance optimization model between the sample and the decision boundary, which makes full use of the sample's prior information. In addition, we also put forward an effective method to filter noise, the main idea of which is to judge the noise by finding the nearest neighbors of the minority class. In order to evaluate the strength of the proposed method, we not only carry out experiments on standard datasets but also apply it to emotional classification problems with different imbalance rates (IR). Experimental results show that the proposed general framework is superior to traditional methods in accuracy, recall and G-means.

翻译：类别不平衡是分类领域中最常见且重要的问题之一。在不平衡数据集上训练的情感分类模型容易导致不可靠的预测。传统机器学习方法倾向于偏向多数类，导致模型中缺乏少数类信息。此外，大多数现有模型会产生异常灵敏度问题或性能下降。我们提出了一种基于自适应代价敏感性和递归去噪的鲁棒学习算法，这是一个通用框架，可集成到大多数随机优化算法中。所提方法利用样本与决策边界之间的动态核距离优化模型，充分挖掘样本先验信息。另外，我们提出了一种有效的噪声过滤方法，其核心思想是通过寻找少数类的最近邻来判别噪声。为评估所提方法的优劣，我们不仅在标准数据集上进行了实验，还将其应用于不同不平衡率的情感分类问题。实验结果表明，所提出的通用框架在准确率、召回率和G-means指标上均优于传统方法。