Incomplete data evidential classification with inconsistent distribution.

The classification analysis of incomplete data is an important and challenging topic in machine learning. Many approaches have been devised to cope with incomplete data. They usually consider that the training and test sets are independent and identically distributed, but the distribution of training and test sets may be inconsistent caused by missing values in applications. In this paper, we propose a novel evidential classification approach to address such a problem based on the Dempster-Shafer theory. First, attributes with missing values are combined with other high correlation attributes to generate different subsets, and they are imputed by K nearest neighbors (KNNs) in subsets. Basic classifiers trained by edited subsets are employed to classify the test sample. Second, the mixed discounting factor composed of the importance and reliability factors is designed to calibrate classification results of the test sample. The importance is evaluated by the difference of distribution between training and test subsets, and the reliability is quantified by minimizing the deviation between classification results of training samples and the truth. Different classification results are combined with mixed discounting factors by the Dempster-Shafer (DS) fusion rule thereby making the final decision. We conduct extensive experiments with several real incomplete data, and the results show that the proposed approach yields more promising and stable performance with respect to other typical approaches. [ABSTRACT FROM AUTHOR]