基于放松区间优势的不完整数据分类

摘要

To solue the problem of declining proportion of clear samples in the tofal when using Naive Credal Classifier, this paper improves conservative inference rule, and proposes an incomplete data classification model based on relaxed conservative inference rule. Simulation results of comparative experiment with Naive Bayesian Classifier and Naive Credal Classifier verify the effectiveness of this classification model. Besides, the style identification as the application background, comparative experimental results further show that this classifier has better overall performance on the style identification data set.%针对朴素信念不完整数据分类算法中保守推理规则过于严格导致明确分类样本比例下降的的情况,定义了放松的区间优势,并提出了基于放松区间优势的不完整数据分类模型,与朴素贝叶斯分类和朴素信念分类算法的对比实验结果表明本文提出的分类模型有效地提高了明确分类样本比例,在明确分类样本上的正确率优于朴素贝叶斯分类,与朴素信念分类相当.此外还以文体风格识别作为应用背景进行了实证研究,对比实验结果进一步表明对于文体风格识别数据集,放松区间优势的朴素信念分类算法具有较理想的综合性能.