A New Classifier to Deal with Incomplete Data

Classification is a very important research topic in knowledge discovery and machine learning. Decision-tree is one of the well-known data mining methods that are used in classification problems. But sometimes the data set for classification contains vectors missing one or more of the feature values, and is called as incomplete data. Generally, the existence of incomplete data will degrade the learning quality of classification models. If the incomplete data can be dealt well, the classifier can be used to real life applications. So handling incomplete data is important and necessary for building a high quality classification model. In this paper a new decision tree is proposed to solve the incomplete data classification problem and it has a very good performance. At the same time, the new method solves two other important problems: rule refinement problem and importance preference problem, which ensures the outstanding advantages of the proposed approach. Significantly, this is the first classifier which can deal with all these problems at the same time.