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Important Facts and Observations about Pairwise Comparisons (the special issue edition)

Koczkodaj W. W., Redlarski G., Szybowski J., Wajch E., Mikhailov L., Soltys M., Tamazian G., Yuen K. K. F.

Fundamenta Informaticae

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2016

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Vol. 144, nr 3/4

291--307

EN

This study has been inspired by numerous requests for clarification from researchers who often confuse Saaty’s Analytic Hierarchy Process (AHP) with the pairwise comparisons (PC) method, taking AHP as the only representation of PC. This study should be regarded as an interpretation and clarification of past investigations of PC. In addition, this article is a reflection on general PC research at a higher level of abstraction: the philosophy of science. It delves into the foundations and implications of pairwise comparisons. Some results of this study are based on a recently published work by Koczkodaj and Szwarc. Finally, open problems have also been reported for future research.

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Medical Knowledge Mining from Image Data : Synthesis of Medical Image Assessments for Early Stroke Detection

Koczkodaj W. W., Przelaskowski A., Szopinski K. T.

Machine Graphics and Vision

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2010

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Vol. 19, No. 3

283-298

EN

The key issue of this study is synthesis of medical images and expert knowledge for early detection of a medical condition, such as stroke or cancer. Such synthesis is a missing link for making decisions during the diagnostic process. Knowledge mining in image databases can be enhanced by computing the relative importance of image features using pairwise comparisons. Computed weights can be systematically used for synthesis of various image features present in the same or different images.

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Classifying Visual Objects with the Consistency-Driven Pairwise Comparisons Method

Koczkodaj W. W., Robidoux N., Tadeusiewicz R.

Machine Graphics and Vision

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2009

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Vol. 18, No. 2

143-154

EN

The classification of the various image features or visual objects can be carried out by the consistency-driven pairwise comparisons method based on their relative importance. A key issue in the proposed approach is a weight-based synthesis for combining various image features. When compared with the traditional experience-based linear assignment method, the proposed approach is more effective and easy to communicate.

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About Business Decision Making by A Consistency-Driven Pairwise Comparisons Method

Koczkodaj W. W., LeBrasseur R., Wassilew A., Tadeusziewicz R.

Journal of Applied Computer Science

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2009

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Vol. 17, nr 1

55-70

EN

Writing this paper has been inspired by the most recent economic crises in the world. Better decision making methods are more needed now than ever before. This study presents an innovate approach to the assessment of management capability in businesses. It is based on the consistency-driven pairwise comparisons method. A proposed conceptual model of performance is flexible and adaptable to different requirements and preconditions (e.g., grant or loan applications). Considering the complexity, a hierarchical structure is used and an inconsistency analysis is performed for all the levels of the structure. The pairwise comparisons method synthesizes together performance assessments assessed at two levels (in our case; there may be more levels in general). The method of consistency-driven pairwise comparisons can be combined with other quantitative and qualitative assessment methods (including brainstorming and Delphi method). Non measurable criteria which often bypassed in other approaches, can be included in the presented model. The consistency-driven pairwise comparisons method contributes to the reliability of assessment through the consistency analysis and solid statistical studies show the accuracy improvement. The management capability merit index (MC-merit) and a procedure for computing it are introduced.