Learning Belief Networks in the Presence of Missing Values and Hidden Variables

Handling missing values when tackling real-world datasets is a great challenge arousing the interest of many scientific communities. Many works propose completion methods or implement new data mining techniques tolerating the presence of missing values. ...

Using five medical datasets we detected the influence of missing values on true positive rates and classification accuracy. We randomly marked more and more values as missing and tested their effects on classification accuracy. The classifications were ...

In this paper, a new approach to working with missing attribute values in inductive learning algorithms is introduced. Three fundamental issues are studied: the splitting criterion, the allocation of values to missing attribute values, and the ...