Function finding in classification learning

The paper describes a method for extending domain models in classification learning by deriving new attributes from existing attributes. The process starts by finding functional regularities within each class. Such regularities are then treated as additional attributes in the subsequent classification learning process. The research revealed that these techniques can reduce the number of clauses required to describe each class, enable functional regularities between attributes to be incorporated in the classification procedures and, depending on the nature of data, significantly increase the coverage of class descriptions and improve the accuracy of classifying novel instances when compared to classification learning alone.