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A diagnostic system based on a multi-decision approximate rules model

Ray R. Hashemi, F. Choobineh, John R. Talburt, William Slikker, Merle G. Paule

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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.

About this research paper

What this paper is about

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.

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Available 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.

Key concepts: Decision tree, Decision system, Rough set, Computer science, Decision rule, Contrast (vision), Data mining, Decision support system

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