代价敏感深度学习方法研究综述

Survey on Cost-sensitive Deep Learning Methods

代价敏感(cost-sensitive)学习方法能够有效缓解分类任务中的数据不平衡问题,已经被成功应用于各种传统机器学习技术中。随着深度学习技术的不断发展,代价敏感方法重新成为了研究热点。将深度学习与代价敏感方法相结合,不仅能够突破传统机器学习技术的限制,同时能够提高模型对数据的敏感性和分类的准确性,尤其是当数据中存在一定的不平衡性时。然而,如何有效地将两者进行结合成为了研究的重点和难点。研究学者从网络结构、损失函数和训练方法等多方面入手,不断提高深度学习结合代价敏感方法模型的性能。文中针对深度学习与代价敏感方法相结合的发展历程进行详细阐述,对几种具有创新性的模型进行了分析,并对比了模型的分类性能,最后对深度学习与代价敏感方法相结合的发展趋势进行了探讨。

Cost-sensitive learning method can effectively alleviate the problem of data imbalance in classification tasks and has been successfully applied to various traditional machine learning techniques.With the continuous development of deep learning technology,cost-sensitive method has become a research hotspot again.The combination of deep learning with cost-sensitive methods can not only breaks through the limitations of traditional machine learning technology,but also improve the data sensitivity and classification accuracy of the model,especially when there is a certain imbalance in the data.However,how to effectively combine the above two factors has become the focus and difficulty of the research.From the aspects of network structure,loss function and training method,researchers have improved the performance of the deep learning model combined with cost-sensitive method.In this paper,the development of the combination of deep learning and cost-sensitive method was described in detail,several innovative models were analyzed and the classification performance of these model was compared.Finally,the development trend of combination of deep learning and cost-sensitive method was discussed.