A GENERAL AND FLEXIBLE FUZZY CLASSIFICATION FRAMEWORK AND ITS APPLICATION TO MEDICAL DIAGNOSIS

Ioannis Gadaras and Ludmil Mikhailov
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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

Chapter 17 in Marketing and Management Sciences, 2010, pp 91-95 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractThe 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.

Keywords: Management, Organizational Behavior, Marketing; Negotiation, Dynamic Models, International Business, Strategic Business, Human Resource, (search for similar items in EconPapers)
JEL-codes: O30 (search for similar items in EconPapers)
Date: 2010
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