一种新的多分类代价敏感算法

A New Multi-class Cost-sensitive Algorithm

为了提高代价敏感分类算法MetaCost的准确率,降低错分代价,提出了多类别问题下的一种代价敏感分类算法(简称D-MetaCost算法).该算法利用MetaCost算法,通过多次取样生成多个模型,依据它们的分类准确率,选择其中准确率较高的前几个基分类器,将它们与最后阶段新生成的分类器聚集在一起得到最终分类模型.实验表明,D-MetaCost算法在准确率和代价方面比经典的MetaCost算法有明显的改进和提高.

Cost-sensitive classification is an important research topic in the classification problem.In order to improve the accuracy of MetaCost, which serves as a cost-sensitive classification algorithm, and reduce its misclassification cost, we propose a new cost-sensitive algorithm, called D-MetaCost, for multi-class problems.In D-MetaCost algorithm, we can calculate the accuracy of multiple mod- els generated in the beginning of MetaCost algorithm,and select first few base classifiers with higher accuracy, then integrate them together with the new model of the last stage to obtainthe final classification model.Experimental results show that the proposed al- gorithm enjoys obvious improvements in accuracy and cost in comparison with the classical MetaCost algorithm.