Representing, adapting and reasoning with uncertain, imprecise and vague information

Vouros, George A.

doi:10.1016/s0957-4174(00)00031-2

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…Analyses of the classical Dempster–Shafer theory (Gordon & Shortliffe, 1984; Beynon et al , 2000) show that the theory can be used to model medical diagnosis. We present a model that differs from that previously presented (Vouros, 2000) and avoids the problem of a multiplication of focal elements. The notation is similar to that used by Kacprzyk and Fedrizzi (1994), as this seems to be the most convenient for modelling medical diagnosis.…”

Section: A Model Of Medical Diagnosis In the Dempster–shafer Theorymentioning

confidence: 99%

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Combining knowledge from different sources

Straszecka¹

2010

Expert Systems

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The present paper deals with the problem of an assessment of symptoms in medical diagnosis. A unified interpretation of symptoms is necessary to estimate their significance in a diagnosis. Yet, even if they are properly defined, different evaluations of them based on experts' knowledge or statistical estimation are possible. The present study aims at combining evaluations that may originate from an expert or can be found from statistical features of the data, as well as those determined for 'easy' and 'difficult' diagnostic cases. A model of diagnostic inference is proposed in the framework of the Dempster-Shafer theory extended for fuzzy focal elements. The basic probability assignment defined in this theory estimates weights of symptoms. Two basic probability assignments can be created and then combined. In this way weights of symptoms represent knowledge common for two kinds of data or obtained from an expert and from data. Thus, a combination of heuristics and data mining results becomes possible. An algorithm of the basic probability assignment calculation is suggested and tested for medical data: a database from the internet and individually gathered data.

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Section: A Model Of Medical Diagnosis In the Dempster–shafer Theorymentioning

confidence: 99%

“…However, exacerbation of a symptom is important in inference. Uncertainty plays a crucial role in representing the knowledge and experience of humans (Vouros, 2000). Therefore, the Dempster–Shafer theory needs to be extended.…”

Section: Introductionmentioning

confidence: 99%

Combining knowledge from different sources

Straszecka¹

2010

Expert Systems

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“…They attempted to combine the ideas in the D–S theory with the AHP. The following references make useful contributions towards the use of ER for representing and managing uncertainty in Decision Analysis (Xu, 1997; Denoeux, 1999; Murphy, 2000; Vouros, 2000).…”

Section: Er Approachmentioning

confidence: 99%

“…The quantity m (Θ) is a measure of the portion of the total belief that remains unassigned after the commitment of belief to all subsets of Θ. m (Θ) is called ignorance or ‘ missing ’ information in the D–S theory (Vouros, 2000; Smets, 1999). If m ( X )= s ( X ⊆Θ) and it is also known that no belief is assigned to other subsets of Θ, then m (Θ)=1 – s .…”

Section: Er Approachmentioning

confidence: 99%

Data transformation in the evidential reasoning‐based decision making process

Sönmez

2007

Int Trans Operational Res

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This paper describes the application of an evidential reasoning (ER)-based decision making process to multiple-criteria decision making (MCDM) problems having both quantitative and qualitative criteria. The ER approach is based on the decision theory and the theory of evidence and it uses the concept of 'degree of belief ' to assess decision alternatives on each attribute. When faced with MCDM problems, evaluation and selection or ranking of alternatives appear to be both challenging and vital to arrive at a rational and robust decision. In the presence of both qualitative and quantitative evaluations in an MCDM problem, it is necessary, when using the ER-based decision making process, to transform or convert quantitative data into a belief structure using a number of grades so that the converted belief structure and the original quantitative data are equivalent in values or utilities. This paper suggests three scenarios for data transformation and examines how the ranking of decision alternatives is changed when different scenarios of data transformation are used. Ranking of UK universities using the ER approach is illustrated as an example.

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“…The above-mentioned approaches provide advanced for- malizations for modelling uncertainty in knowledge-based systems. However, because of the limitations and weaknesses reported above, the current work utilizes the approach for knowledge representation proposed by Vouros (2000) that (a) strongly supports assessment of the operational problems of an AS plant with robustness and computational efficiency, (b) makes the encoded knowledge comprehensive and provides results that are intuitive to experts, (c) greatly facilitates knowledge acquisition from experts, and (d) models vague statements.…”

Section: Knowledge Representationmentioning

confidence: 99%

Knowledge representation in an activated sludge plant diagnosis system

Vouros¹,

Pantelakis²,

Lekkas³

2000

Expert Systems

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This paper focuses on the knowledge representation framework utilized by an integrated wastewater treatment expert system for diagnosing operational problems of an activated sludge plant. The system deals with events that may occur in all the units of an activated sludge plant and exploits on-line measurements, observations formed from data provided by laboratory analyses and empirical observations in an integrated manner. The system provides assistance to human experts to control the activated sludge process. It has been tested and evaluated in the pilot activated sludge plant of the Water and Air Quality Laboratory in the University of the Aegean.

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Representing, adapting and reasoning with uncertain, imprecise and vague information

Cited by 7 publications

References 9 publications

Combining knowledge from different sources

Combining knowledge from different sources

Data transformation in the evidential reasoning‐based decision making process

Knowledge representation in an activated sludge plant diagnosis system

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