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A General And Flexible Fuzzy Classification Framework And Its Application To Medical Diagnosis

In: Marketing And Management Sciences

Author

Listed:
  • IOANNIS GADARAS

    (School of Computer Science/Manchester Business School, University of Manchester, Lamb Building Booth Street East, ADJ437, United Kingdom)

  • LUDMIL MIKHAILOV

    (School of Computer Science, University of Manchester/ Manchester Business School, Decision and Data Engineering Research Group, Lamb Building Booth Street East, ADJ437, United Kingdom)

Abstract

The current paper presents the formalization and functionality of a general classification methodology that attempts to automatically extract fuzzy classification rules directly from labeled numerical data. Based on an iterative hierarchical fuzzy partitioning process, the proposed method attempts to find an optimum feature partitioning approach that can increase the overall accuracy and minimizing the required number of rules. This is achieved by the introduction of two threshold values the first of which controls the depth of the partitioning where needed and a second one that assigns suitable weights to the a selected number of rules.

Suggested Citation

  • Ioannis Gadaras & Ludmil Mikhailov, 2010. "A General And Flexible Fuzzy Classification Framework And Its Application To Medical Diagnosis," World Scientific Book Chapters, in: Damianos P Sakas & Nikolaos Konstantopoulos (ed.), Marketing And Management Sciences, chapter 17, pages 91-95, World Scientific Publishing Co. Pte. Ltd..
    • Handle: RePEc:wsi:wschap:9781848165106_0017
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    More about this item

    Keywords

    Management; Organizational Behavior; Marketing; Negotiation; Dynamic Models; International Business; Strategic Business; Human Resource;
    All these keywords.

    JEL classification:

    • O30 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - General

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