Supporting preprocessing and postprocessing for machine learning algorithms: a workbench for ID3

Abstract

Abstract Inductive learning algorithms have been suggested as alternatives to knowledge acquisition for expert systems. However, the application of machine learning algorithms often involves a number of subsidiary tasks to be performed as well as algorithm execution itself. It is important to help the domain expert manipulate his or her data so they are suitable for a specific algorithm, and subsequently to assess the algorithm results. These activities are often called preprocessing and postprocessing. This paper discusses issues related to the application of the ID3 algorithm, an important representative of the inductive learning family. A prototype workbench which has been developed to provide an integrated approach to the application of ID3 is presented. The design rationale and the potential use of the system is justified. Finally, future directions and further enhancements of the workbench are discussed.