Supervised Learning with Qualitative and Mixed Attributes

Building classification tools to discriminate between good and bad credit risks is a supervised learning task which can be solved using different approaches (Graf and Nakhaeizadeh (1994)). In constructing such tools, generally, a set of training data, containing qualitative and quantitative attributes, is used to learn the discriminant rules. In real world of credit applications a lot of the available information about the customer and his behaviour of payment appears in qualitative, categorical attributes.