Data Decomposition and Decision Rule Joining for Classification of Data with Missing Values

In this paper we present a new approach to handling incomplete information and classifier complexity reduction. We describe a method, called DRJ, that performs data decomposition and decision rule joining to avoid the necessity of reasoning with missing attribute values. In the consequence more complex reasoning process is needed than in the case of known algorithms for induction of decision rules. The original incomplete data table is decomposed into sub-tables without missing values. Next, methods for induction of decision rules are applied to these sets. Finally, an algorithm for decision rule joining is used to obtain the final rule set from partial rule sets. Using DRJ method it is possible to obtain smaller set of rules and next better classification accuracy than classic decision rule induction methods. We provide an empirical evaluation of the DRJ method accuracy and model size on data with missing values of natural origin.