A new classifier for numerical incomplete data

Classification of the numerical data is a very important research topic in machine learning. But the incomplete data is very common in real world application. And the existence of incomplete data degrades the learning quality of classification models. But the existence of incomplete data always decrease the quality of classification models, To show the definition of missing data more intuitively, The example is taken like this: If Xl=(l,2,3,4), then (?,2,3,4) is X with 25% incomplete data, and (1,?,?,4) is XI with 50% incomplete data. In this paper a new classifier is proposed to solve the incomplete data classification problem and it has an outstanding performance.