Inductive Learning Support for Decision Making

In this paper we review the applicability of representative inductive machine learning approaches in multicriteria decision making. We limit our review to four systems. We use SICLA and KBG as representative conceptual clustering systems and ID3 and CN2 as representative learning from examples systems. We demonstrate our results by way of two real world decision making exemplars. The first exemplar concerns the evaluation of retail outlets [15]. The second exemplar concerns venture capital assessment [16]. We discuss the conditions under which inductive learning methodologies can be effectively implemented to support decision making.