  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.
