Fuzzy Algorithms for Decision Making and Data Analysis

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 October 2013) | Viewed by 12557

Special Issue Editor

Special Issue Information

Dear Collegues,

Real-world problems are always complex and with the presence of uncertainty. The fuzzy technologies embedded into the methodologies of data analysis and decision making processes may help deal with such difficult problems. We invite researchers and practitioners to submit their original research and review articles that explore the most recent advances in the applications of fuzzy data analysis and decision making. Potential topics include, but are not limited to:

• Dynamic data analysis
• Pattern recognition
• Fuzzy modeling
• Fuzzy decision support system
• Fuzzy data envelopment analysis
• Fuzzy data filter
• Group decision making
• Interactive decision making
• Intelligent data analysis
• Multiple attribute decision making
• Multiple objective decision making

Prof. Dr. Toly Chen
Guest Editor

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Published Papers (1 paper)

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Research

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Article
Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems
by Shabnam Jabari and Yun Zhang
Algorithms 2013, 6(4), 762-781; https://doi.org/10.3390/a6040762 - 12 Nov 2013
Cited by 57 | Viewed by 11142
Abstract
The aim of this research is to present a detailed step-by-step method for classification of very high resolution urban satellite images (VHRSI) into specific classes such as road, building, vegetation, etc., using fuzzy logic. In this study, object-based image analysis is used [...] Read more.
The aim of this research is to present a detailed step-by-step method for classification of very high resolution urban satellite images (VHRSI) into specific classes such as road, building, vegetation, etc., using fuzzy logic. In this study, object-based image analysis is used for image classification. The main problems in high resolution image classification are the uncertainties in the position of object borders in satellite images and also multiplex resemblance of the segments to different classes. In order to solve this problem, fuzzy logic is used for image classification, since it provides the possibility of image analysis using multiple parameters without requiring inclusion of certain thresholds in the class assignment process. In this study, an inclusive semi-automatic method for image classification is offered, which presents the configuration of the related fuzzy functions as well as fuzzy rules. The produced results are compared to the results of a normal classification using the same parameters, but with crisp rules. The overall accuracies and kappa coefficients of the presented method stand higher than the check projects. Full article
(This article belongs to the Special Issue Fuzzy Algorithms for Decision Making and Data Analysis)
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