Knowledge acquisition in incomplete information systems: A rough set approach

Leung, Yee; Wu, Wei-Zhi; Zhang, Wenxiu

doi:10.1016/j.ejor.2004.03.032

Cited by 271 publications

(81 citation statements)

References 19 publications

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“…Pawlak's rough set model may be generalized to nonequivalence relations. The extensions of Pawlak's rough set model may be used in reasoning and knowledge acquisition in incomplete information systems and incomplete fuzzy systems [13,14,15,16,17,18,19,20,21].…”

Section: Introductionmentioning

confidence: 99%

Knowledge Reduction in Incomplete Information Systems Based on Dempster-Shafer Theory of Evidence

2006

Rough Sets and Knowledge Technology

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Abstract. Knowledge reduction is one of the main problems in the study of rough set theory. This paper deals with knowledge reduction in incomplete information systems based on Dempster-Shafer theory of evidence. The concepts of plausibility and belief consistent sets as well as plausibility and belief reducts in incomplete information systems are introduced. It is proved that a plausibility consistent set in an incomplete information system must be a consistent set and an attribute set in an incomplete information system is a belief reduct if and only if it is a classical reduct.

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Section: Introductionmentioning

confidence: 99%

Knowledge Reduction in Incomplete Information Systems Based on Dempster-Shafer Theory of Evidence

2006

Rough Sets and Knowledge Technology

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“…The basic task is to establish a sound, efficient, accurate and comprehensive knowledge base to meet the expert system's requirements for problem solving (Li et al 2015, Walsh 2015. Many techniques have been developed for knowledge acquisition from domain experts (Boose 1985, Leung et al 2006, Marcus 2013, Tudorache et al 2013. In this study, we analyzed and summarized the body of knowledge by conducting literature reviews and interviewing experts by a questionnaire to analyze diseases ( Fig.…”

Section: Knowledge Acquisition and Representationmentioning

confidence: 99%

Horse-Expert: An aided expert system for diagnosing horse diseases

Qin¹,

Xiao²,

Gao³

et al. 2016

Polish Journal of Veterinary Sciences

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In contrast to the rapid development of the horse husbandry in China, the ability of horse veterinarians to diagnose diseases has not been improved and only a few domain experts have considerable expertise. At present, many expert systems have been developed for diseases diagnosis, but few for horse diseases diagnosis have been studied in depth. This paper presents the design and development of a computer-aided expert system for diagnosing horse diseases. We suggest an approach for diagnosis of horse diseases based on the analysis of diagnostic characteristics and the experiential knowledge of domain experts. It is based on using evidence-weighted uncertainty reasoning theory, which is a combination of evidence theory and an uncertainty pass algorithm of confidence factors. It enables drawing of inferences with atypical clinical signs and the uncertainty of the user's subjective understanding. It reduces the influence of subjective factors on diagnostic accuracy. The system utilizes a user friendly interface for users and requests a confidence factor from users when feedback is given to the system. Horse-Expert combines the confidence factors with weight factors assigned to clinical signs by experts during the knowledge acquisition process to make diagnostic conclusions. The system can diagnose 91 common horse diseases, and provides suggestions for appropriate treatment options. In addition, users can check the medical record through statistical charts. The system has been tested in seven demonstration areas of Xinjiang province in northwestern China. By constantly maintaining and updating the knowledge base, the system has potential application in veterinary practice.

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“…The extensions of Pawlak's rough set model may be used in reasoning and knowledge acquisition in incomplete decision tables [5,8,11,[18][19][20][21][22].…”

Section: Classical Definitions Of Lower and Upper Approximations Sommentioning

confidence: 99%

A Rough Set Approach for the Discovery of Classification Rules in Interval-Valued Information Systems

Leung

Fischer

et al. 2008

SSRN Journal

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A novel rough set approach is proposed in this paper to discover classification rules through a process of knowledge induction which selects optimal decision rules with a minimal set of features necessary and sufficient for classification of real-valued data. A rough set knowledge discovery framework is formulated for the analysis of interval-valued information systems converted from real-valued raw decision tables.The optimal feature selection method for information systems with interval-valued features obtains all classification rules hidden in a system through a knowledge induction process. Numerical examples are employed to substantiate the conceptual arguments.

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Knowledge acquisition in incomplete information systems: A rough set approach

Cited by 271 publications

References 19 publications

Knowledge Reduction in Incomplete Information Systems Based on Dempster-Shafer Theory of Evidence

Knowledge Reduction in Incomplete Information Systems Based on Dempster-Shafer Theory of Evidence

Horse-Expert: An aided expert system for diagnosing horse diseases

A Rough Set Approach for the Discovery of Classification Rules in Interval-Valued Information Systems

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