Fuzzy Rule Extraction for Determining Creditworthiness of Credit Applicants

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dc.contributor.authorSteve G Romanuiken_US
dc.date.accessioned2004-10-21T14:28:52Zen_US
dc.date.accessioned2017-01-23T07:00:41Z
dc.date.available2004-10-21T14:28:52Zen_US
dc.date.available2017-01-23T07:00:41Z
dc.date.issued1992-10-01T00:00:00Zen_US
dc.description.abstractThe 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-defined fuzzy membership functions or pre-selection of important input features is required. The affect 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.en_US
dc.format.extent139555 bytesen_US
dc.format.extent205159 bytesen_US
dc.format.mimetypeapplication/pdfen_US
dc.format.mimetypeapplication/postscripten_US
dc.identifier.urihttps://dl.comp.nus.edu.sg/xmlui/handle/1900.100/1261en_US
dc.language.isoenen_US
dc.relation.ispartofseriesTR20/92en_US
dc.titleFuzzy Rule Extraction for Determining Creditworthiness of Credit Applicantsen_US
dc.typeTechnical Reporten_US
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