Abstract

The Machine Learning Toolbox (MLT), an Esprit project (P2154), provides an integrated toolbox of ten Machine Learning (ML) algorithms. One distinct component of the toolbox is Consultant, an advice-giving expert system, which assists a domain expert to choose and use a suitable algorithm for his learning problem. The University of Aberdeen has been responsible for the design and implementation of Consultant.

Consultant's knowledge and domain is unusual in several respects. Its knowledge represents the integrated expertise of ten algorithm developers, whose algorithms offer a range of ML techniques; but also some algorithms use fairly similar approaches. The lack of an agreed ML terminology was the initial impetus for an extensive, associated help system. From an MLT user's point of view, an ML beginner requires significant assistance with terminology and techniques, and can benefit from having access to previous, successful applications of ML to similar problems; but in contrast a more experienced user of ML does not wish constant supervision. This paper describes Consultant, discusses the methods used to achieve the required flexibility of use, and compares Consultant's similarities and distinguishing features with more standard expert system applications.

INTRODUCTION

The Machine Learning Toolbox (MLT), an Esprit project (P2154), provides an integrated toolbox of ten Machine Learning (ML) algorithms. One distinct component of the toolbox is Consultant, an advice-giving expert system. It provides domain experts with assistance and guidance on the selection and use of tools from the toolbox, but it is specifically aimed at experts who are not familiar with ML and its design has focused on their needs.