Ordered Estimation of Missing Values

Abstract

When attempting to discover by learning concepts embedded in data, it is not uncommon to find that information is missing from the data. Such missing information can diminish the confidence on the concepts learned from the data. This paper describes a new approach to fill missing values in examples provided to a learning algorithm. A decision tree is constructed to determine the missing values of each attribute by using the information contained in other attributes. Also, an ordering for the construction of the decision trees for the attributes is formulated. Experimental results on three datasets show that completing the data by using decision trees leads to final concepts with less error under different rates of random missing values. The approach should be suitable for domains with strong relations among the attributes, and for which improving accuracy is desirable even if computational cost increases.