基于分布不完整数据选择性分类器

Distribution-Based Selective Classifiers for Incomplete Data

通过删除数据集中的无关属性和冗余属性构建的选择性分类器可以有效地提高分类精度和效率.由于处理不完整数据的复杂性,已有的选择性分类器大都是针对完整数据的.然而,现实中的数据通常是不完整的并且包含许多冗余属性或无关属性.为解决这一问题,在构建的不完整数据分类器DBNB的基础上给出了一种有效的选择性分类器:SDBNB.在12个标准的不完整数据集上的实验结果显示,SDBNB的分类准确率比分类效果较好的选择性不完整数据分类器SNB和SRBC平均高出0.69%和0.58%,而其标准离差比SNB和SRBC平均低0.11和0.05.这表明SDBNB不仅有较高的分类准确率,而且分类效果更稳定.

Selective classifiers are a kind of algorithms that can effectively improve the accuracy and efficiency of classification by deleting irrelevant or redundant attributes of a data set. Due to the complexity of processing incomplete data, however, most of them deal with complete data. Yet actual data are often incomplete and have many redundant or irrelevant attributes, a selective classifier for incomplete data （SDBNB）, which is based on a newly constructed Bayes classifier （DBNB）, is presented. Experiments results from twelve benchmark incomplete data sets show that the average accuracy of SDBNB is 0.69 percent and 0.58 percent higher than that of the effective selective classifiers： SNB and SRBC. Furthermore, its standard deviation is 0.11 and 0.05 lower than that of SNB and SRBC. This shows that not only SDBNB has higher accuracy, but also performs more stably as well.