Special Issue "Fuzzy Algorithms for Decision Making and Data Analysis"

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

Deadline for manuscript submissions: closed (15 October 2013).

Special Issue Editor

Prof. Dr. Tin-Chih Toly Chen
E-Mail Website
Guest Editor
Department of Industrial Engineering and Management National Chiao Tung University 1001, University Rd., Hsinchu City, Taiwan
Interests: uzzy and neural computing; competitiveness analysis; cloud and ubiquitous manufacturing; operations research; semiconductor manufacturing; ambient intelligence
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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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

Open AccessArticle
Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems
Algorithms 2013, 6(4), 762-781; https://doi.org/10.3390/a6040762 - 12 Nov 2013
Cited by 28
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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