Fuzzy Rule Extraction for Determining Creditworthiness of Credit Applicants

The main objective of this research paper is to provide an empirical analysis of the hybrid symbolic/connectionist expert system development tool SC-net to act as a viable system for acquiring expert system knowledge by means of learning. The task to be studied is the prediction of creditworthiness for credit seeking applicants. The creditworthiness domain-unlike many other domains studied by the machine learning community-contains both uncertainties in the inputs and outputs. Apart from showing SC-net's ability to derive human acceptable models for this data, strong emphasis is placed on deriving rules that can adequately describe the imprecision inherent in such domains. No a priori domain knowledge, such as pre-deened fuzzy membership functions or pre-selection of important input features is required. The aaect of training set size on number of rules and attributes per rule is addressed and a sample set of extracted rules with derived membership functions is provided. In all cases acceptable models for determining creditworthiness are derived. The herein described experimental results should further strengthen SC-net's ability to act as a knowledge acquisition tool for obtaining acceptable expert knowledge in uncertain domains.