A DIAGNOSTIC SYSTEM BASED ON A MULTI-DECISION APPROXIMATE RULES MODEL Ray R. Hashemil, 2 , Fred Choobineh 3, John Talburt 1, William Slikker 2, and Merle G. Paule 2 1Department of Computer and Information Science University of Arkansas at Little Rock Little Rock, AR 72204 rmhashemi@ualr.edu 2Division of Nerotoxicology National Center for Toxicological Research/FDA Jefferson, AR 72079-9502 mpaule@,fdant.nctr.fda.gov 3Department of Industrial Engineenng University of Nebraska at Lincoln Lincoln, NE 68508 fchoobineh@unl.edu KEY WORDS: Diagnostic System, Rough Sets, Modified Rough Sets, Approximate rule, MultiDecision Approximate Rule. ABSTRACT Modified Rough Sets (MRS)-based diagnostic systems eliminated many of the limitations of Rough Sets (RS)-based, statistical-based, and decision tree-based systems and because of that, they have a better performance. In contrast with the other systems, MRS-based diagnostic systems have potential to handle Multi-Decision Approximate (MDA) rules. In this paper, we (1) develop a diagnostic model based on MDA rules and (2) evaluate the classification power of the model. 1. INTRODUCTION The Rough Sets (RS) and Modified Rough sets (MRS) based systems prove to be worthy of consideration in diagnostic systems. To be more specific, these approaches deliver a better performance than systems based on Neural Networks, Decision trees, and the statistical models
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A diagnostic system based on a multi-decision approximate rules model
Hashemi, Ray R.; Choobineh, Fred; Talburt, John; Slikker, William; Paule, Merle G.
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, Volume 17 (1)
Association for Computing Machinery – Apr 1, 1997
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