Handling unknown and imprecise attribute values in propositional rule learning: A feature-based approach

Abstract

Rule learning systems use features as the main building blocks for rules. A feature can be a simple attribute-value test or a test of the validity of a complex domain knowledge relationship. Most existing concept learning systems generate features in the rule construction process. However, the separation of feature generation and rule construction processes has several theoretical and practical advantages. In particular, the proposed transformation from the attribute to the feature space motivates a novel, theoretically justified procedure for handling of unknown attribute values. This approach suggests also a novel procedure for handling imprecision of numerical attributes. The possibility of controlling the expected imprecision of numerical attributes during the induction process is a novel machine learning concept which has a high application potential for solving real world problems. © 2008 Springer Berlin Heidelberg.