Decision making on creditworthiness, using a fuzzy connectionist model

Abstract

In this paper we present an unpolished expert system development tool, based on a connectionist architecture for knowledge representation. Our work is centered around a connectionist expert system, which can be expanded, and updated through learning of sample domain specific cases [1,4]. A cell recruitment learning algorithm [2] capable of forgetting previously learned facts by learning new ones is incorporated. Using this learning mechanism, we let the system learn a knowledge base on classifying the creditworthiness of credit applicants. The knowledge base consisted of 50 credit cases and was obtained from [11]. It will be shown that the fuzznet system is capable of learning such a model, with only a small amount of the cases being presented to it for learning. In all examples learned, an acceptable model was derived (based on the average prognostic error of actual and expected output for creditworthiness). The input features and their corresponding outputs (which were learned) are all fuzzy (uncertain) at the time of learning. So far implementations of connectionist expert systems, either only allowed for crisp inputs when placed in the learning mode, or only supported uncertainty when simulated [1, 8, 9, 10]. This example will further demonstrate the versatility of the fuzznet system, when dealing with uncertainty in the inputs and outputs when being placed in the learn mode, or while being simulated.