In the practical application of classification, how to handle uncertain information for efficient classification is a hot topic. In this paper, in the frame of Dempster–Shafer evidence theory, a new classification method based on the negation of basic probability assignment (BPA) is proposed to implement an effective classification. The proposed method addresses the issue that the values of samples’ attributes cannot clearly point out a certain class in classification problems. For uncertain information modeling, the negation of BPA is adopted to obtain more valuable information in the body of evidence. To measure the uncertain information represented by the negation of BPA, the belief entropy is used for calculating the uncertain degree of each body of evidence. Finally, Dempster’s combination rule is used for data fusion to identify and recognize the unknown class. The effectiveness and efficiency of the new classification method are validated according to experiments on several UCI data sets. In addition, the classification experiment on the data sets with the changing proportion of the training set verifies that the method is robust and feasible.

在分类的实际应用中，如何处理不确定信息以进行有效分类是一个热门话题。本文在Dempster-Shafer证据理论的框架下，提出了一种基于基本概率分配（BPA）取反的新分类方法，以实现有效的分类。所提出的方法解决了样本属性值不能明确指出分类问题中特定类别的问题。对于不确定的信息建模，采用BPA取反可以获取更多有价值的证据。为了测量由BPA取反表示的不确定信息，使用信念熵来计算每个证据主体的不确定程度。最后，Dempster的组合规则用于数据融合，以识别和识别未知类。根据在几个UCI数据集上的实验，验证了新分类方法的有效性和效率。另外，对训练集比例不断变化的数据集进行分类实验，验证了该方法的鲁棒性和可行性。