Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions

Çelikyılmaz, Aslı; Türkşen, I.B.; Aktaş, Ramazan; Doğanay, M. Mete; Ceylan, Nildağ Başak

doi:10.1016/j.eswa.2007.11.039

Cited by 25 publications

(9 citation statements)

References 27 publications

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“…In this work, two new ratio-type cluster validity indices, cviIFC, and cviIFC-C are presented to validate novel Improved Fuzzy Clustering (IFC) [Celikyilmaz and Turksen, 2007b] and IFC for classification problems, IFC-C [Celikyilmaz and Turksen, 2007i] Firstly, we will focus on IFC for regression problems. In IFC algorithm, clusters are identified by cluster prototypes (centers) and their corresponding regression functions.…”

Section: The New Cluster Validity Indicesmentioning

confidence: 99%

Modeling Uncertainty with Fuzzy Logic

Çelikyılmaz

Türkşen

2009

Studies in Fuzziness and Soft Computing

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105

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The issue of second-order uncertainty has received relatively little attention so far, but its intrinsic importance is certain to make it an object of growing attention in coming years. Through their work, the authors have opened a door to wide-ranging applications. They deserve our compliments and congratulations. Berkeley, CA November 30, 2008 Lotfi A. Zadeh XIV PrefaceAfter testing the new fuzzy functions tools on various benchmark problems, the algorithms are successfully applied to model decision processes in two real problem domains: the desulphurization process in steel making and stock price prediction activities. For both of these problems, the proposed methods produce robust and high performance results, which are comparable (if not better) than the best system modeling approaches known in the current literature. Several aspects of the proposed methodologies are thoroughly analyzed to provide a deeper understanding. These analyses show consistency of the results.As a final note, the fuzzy modeling approaches demonstrated in this book may supply suitable framework for engineers, and practitioners for a design of complex systems with fuzzy functions instead of crisp functions, that is to say, for a design of artificial systems based on well known functions, viz. regression, classification, etc. Although in this book we only show examples from economy and production industry, we believe that fuzzy modeling can and should be utilized in many fields of science, including biology, economics, psychology, sociology, history and more. In such fields, many models exist in current research literature and they can be directly transformed into mathematical models using the methodologies presented herein.

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Section: The New Cluster Validity Indicesmentioning

confidence: 99%

Modeling Uncertainty with Fuzzy Logic

Çelikyılmaz

Türkşen

2009

Studies in Fuzziness and Soft Computing

Self Cite

105

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“…As an alternative to FRB, Ç elikyılmaz et al [8] adopted Fuzzy Functions (FF), firstly introduced by Türkşen [28], to classification problems named as Fuzzy Classifier Functions (FCF). These methods are further improved by Ç elikyılmaz et al [9]. In addition to the classification methods mentioned above, there are fuzzy linear regression (FLR) methods.…”

Section: Introductionmentioning

confidence: 99%

Classification models based on Tanaka’s fuzzy linear regression approach: The case of customer satisfaction modeling

Şekkeli

Köksal

Batmaz

et al. 2010

Journal of Intelligent &Amp; Fuzzy Systems

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Fuzzy linear regression (FLR) approaches are widely used for modeling relations between variables that involve human judgments, qualitative and imprecise data. Tanaka's FLR analysis is the first one developed and widely used for this purpose. However, this method is not appropriate for classification problems, because it can only handle continuous type dependent variables rather than categorical. In this study, we propose three alternative approaches for building classification models, for a customer satisfaction survey data, based on Tanaka's FLR approach. In these models, we aim to reflect both random and fuzzy types of uncertainties in the data in different ways, and compare their performances using several classification performance measures. Thus, this study contributes to the field of fuzzy classification by developing Tanaka based classification models.

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“…Step 3: An approximate optimum number of clusters, c*, of IFC algorithm is determined with the cluster validity index, cviFF (Celikyilmaz & Turksen, 2009a;2008c), designed to evaluate the IFC algorithm with: …”

Section: Enhanced Fis With Improved Fuzzy Functionsmentioning

confidence: 99%

Information Extraction from Text – Dealing with Imprecise Data

Burhan¹

2010

Fuzzy Systems

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Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions

Cited by 25 publications

References 27 publications

Modeling Uncertainty with Fuzzy Logic

Modeling Uncertainty with Fuzzy Logic

Classification models based on Tanaka’s fuzzy linear regression approach: The case of customer satisfaction modeling

Information Extraction from Text – Dealing with Imprecise Data

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