Rough Set Theory for Supporting Decision Making on Relevance in Browsing Multilingual Digital Resources

2017 ◽  
pp. 129-139 ◽  
Author(s):  
Jolanta Mizera-Pietraszko ◽  
Jolanta Tancula
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Digital Resources

A User-Centered Medical Device Design Decision Making Approach Using Hybrid Rough Cooperative Game Model

2021 ◽  
Author(s):  
Liting Jing ◽  
Junfeng Ma
Keyword(s):  
Decision Making ◽  
Medical Device ◽  
Set Theory ◽  
Cooperative Game ◽  
Rough Set ◽  
Rough Set Theory ◽  
Design Decision ◽  
Device Design ◽  
Medical Device Design ◽  
User Centered

Abstract With the advancement of new technologies and diverse customer-centered design requirements, the medical device design decision making becomes challenge. Incorporating multiple stakeholders’ requirements into the medical device design will significantly affect the market competitiveness and performance. The classic design decision making approaches mainly focused on design criteria priority determination and conceptual schemes evaluation, which lack the capacity of reflecting the interdependence of interest among stakeholders and capturing the ambiguous influence on the overall design expectations, leading to the unreliable decision making results. In order to relax these constraints in the medical device design, this paper incorporates rough set theory with cooperative game theory model to develop a novel user-centered design decision making framework. The proposed approach is composed of three components: 1) end/professional user needs identification and classification, 2) evaluation criteria correlation diagram and scheme value matrix establishment using rough set theory; and 3) fuzzy coalition utility model development to obtain optimal desirability considering users’ conflict interests. We used a blood pressure meter case study to demonstrate and validate the proposed approach. Compared with the traditional Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach, the proposed approach is more robust.

An approach to evaluation of emergency plans for unconventional emergency events based on soft fuzzy rough set

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 461-473 ◽  
Author(s):  
Sun Bingzhen ◽  
Ma Weimin
Keyword(s):  
Decision Making ◽  
Emergency Management ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Management Decision ◽  
Fuzzy Rough Set ◽  
Emergency Plans ◽  
Content Type ◽  
Emergency Events

Purpose – The purpose of this paper is to present a new method for evaluation of emergency plans for unconventional emergency events by using the soft fuzzy rough set theory and methodology. Design/methodology/approach – In response to the problems of insufficient risk identification, incomplete and inaccurate data and different preference of decision makers, a new model for emergency plan evaluation is established by combining soft set theory with classical fuzzy rough set theory. Moreover, by combining the TOPSIS method with soft fuzzy rough set theory, the score value of the soft fuzzy lower and upper approximation is defined for the optimal object and the worst object. Finally, emergency plans are comprehensively evaluated according to the soft close degree of the soft fuzzy rough set theory. Findings – This paper presents a new perspective on emergency management decision making in unconventional emergency events. Also, the paper provides an effective model for evaluating emergency plans for unconventional events. Originality/value – The paper contributes to decision making in emergency management of unconventional emergency events. The model is useful for dealing with decision making with uncertain information.

Applied Rough Set Logics for Multi-Criteria Decision Analysis in Stock Index Volatility Projection

2011 ◽  
Vol 14 (04) ◽  
pp. 715-735
Author(s):  
Wen-Rong Jerry Ho
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Multiple Criteria Decision Making ◽  
Stock Index ◽  
Rule Based ◽  
Set Up ◽  
Decision Making Model

The main purpose of this paper is to advocate a rule-based forecasting technique for anticipating stock index volatility. This paper intends to set up a stock index indicators projection prototype by using a multiple criteria decision making model consisting of the cluster analysis (CA) technique and Rough Set Theory (RST) to select the important attributes and forecast TSEC Capitalization Weighted Stock Index. The projection prototype was then released to forecast the stock index in the first half of 2009 with an accuracy of 66.67%. The results point out that the decision rules were authenticated to employ in forecasting the stock index volatility appropriately.

scholarly journals Another View on the Middle East Conflict Analysis

2018 ◽  
Vol 8 (9) ◽  
pp. 1545
Author(s):  
Noor Rehman ◽  
Syed Shah ◽  
Abbas Ali ◽  
Sun Jang ◽  
Choonkil Park
Keyword(s):  
Decision Making ◽  
Middle East ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Preference Relation ◽  
Conflict Analysis ◽  
Analysis Model ◽  
Middle East Conflict ◽  
To Come

Decision making is a cognitive process for evaluating data with certain attributes to come up with the best option, in terms of the preferences of decision makers. Conflicts and disagreements occur in most real world problems and involve the applications of mathematical tools dealing with uncertainty, such as rough set theory in decision making and conflict analysis processes. Afterwards, the Pawlak conflict analysis model based on rough set theory was established. Subsequently, Deja put forward some questions that are not answered by the Pawlak conflict analysis model and Sun’s model. In the present paper, using the notions of soft preference relation, soft dominance relation, and their roughness, we analyzed the Middle East conflict and answered the questions posed by Deja in a good manner.

From Basic Research to Applications

2005 ◽  
Vol 9 (5) ◽  
pp. 497-497
Author(s):  
Jorma K. Mattila ◽  
Keyword(s):  
Decision Making ◽  
Differential Evolution ◽  
Set Theory ◽  
Rough Set ◽  
Research Group ◽  
Fuzzy Systems ◽  
Rough Set Theory ◽  
Membership Functions ◽  
Special Issue ◽  
Systems Research

Forty years have passed since Prof. Lotfi A. Zadeh introduced fuzzy set theory in his known article “Fuzzy Sets” in Information and Control, 8, 1965, sparking new development in information technology and automation. This article also formed the roots of the Fuzzy Systems Research Group, an active part of the Laboratory of Applied Mathematics, Lappeenranta University of Technology. Rough set theory, evolutionary computing, and neural computing followed, together with their combinations. This Special Issue presents 10 papers representing these areas. Many of the contributors of this Special Issue belong to the Fuzzy Systems Research Group and others work in close co-operations with this group. The first paper considers the use of linguistically expressed objectives in multicriteria decision-making in selection processes based on topological similarity M-relations between L-sets. The second presents basic ideas and fundamental concepts of rough set theory and considers properties of rough approximations. The third combines Lukasiewicz logics and modifier algebras based on Zadeh algebras, i.e., quasi-Boolean algebras of membership functions. The fourth applies Mö{o}bius transformations, known in complex analysis, to fuzzy subgroups in a topological point of view. The fifth discusses the stability of a classifier based on the Lukasiewicz structure and tests Schweizer and Sklar's implications with an extension to generalized mean to a classification task. The sixth deals with the interpretability problem of first-order Takagi-Sugeno systems and interpolation issues, developing a special two-model configuration. The seventh describes an expert system for defining an athlete's aerobic and anaerobic thresholds that successfully mimics decision-making by sport medicine professionals, with system functionality based on fuzzy comparison measures, generalized means, fuzzy membership functions, and differential evolution. The eighth applies a differential evolution algorithm-based method to training radial basis function networks with variables including centers, weights, and widths. The ninth compares two floating-point-encoded evolutionary algorithms – differential evolution and a generalized generation gap model – using a set of problems with different characteristics. The tenth proposes a new approach for monitoring break tendency of paper webs on modern paper machines, combining linguistic equations and fuzzy logic in a case-based reasoning framework. As the Guest Editor of this Special Issue, I thank the contributors and reviewers for their time and effort in making this special issue possible. I am also grateful to the JACIII editorial board, especially Prof. Kaoru Hirota, the Editors-in-Chief and Managing Editor Kenta Uchino, and the staff of Fuji Technology Press for the opportunity to participate in this work. I also thank Prof. Kaoru Hirota for organizing the reviewing of my paper.

scholarly journals Structure of a decision support subsystem in real estate management

2013 ◽  
Vol 13 (1) ◽  
pp. 56-75 ◽  
Author(s):  
Małgorzata Renigier-Biłozor
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Real Estate ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
The Real ◽  
The Real Estate ◽  
Estate Management ◽  
Real Estate Management

Abstract This study proposes a decision support subsystem in real estate management. Owing to the complex and multi-layered character of the discussed problem, only selected aspects of real estate management are discussed in this paper. The described system will play the role of a relatively simple and effective “assistant” which is expected to maximize the effectiveness of a decision and shorten decision-making time. The author has made an attempt to develop a subsystem as an adviser to subjects operating in the real estate management. This system was developed accounting for and combining the classical economic and real estate market theories with the implementation of non-classical methods in the data mining category in an effort to increase its effectiveness. The rough set theory has been proposed as a tool that supports analytical processes. Fuzzy logic best reproduces expert knowledge, and it is one of the most effective tools for solving “vaguely defined” problems. The given work is an attempt to prove the hypothesis that: the reduction of uncertainty in the real estate management decision-making process is possible by the development of the advisory system based on the rough set theory. The main aim of this work is to increase the efficiency and efficacy of entities operating in the real estate management, thus influencing the effectivness of the entity and management.

Financial Distress Study Based on PSO K-Means Clustering Algorithm and Rough Set Theory

2013 ◽  
Vol 411-414 ◽  
pp. 2377-2383 ◽  
Author(s):  
Peng Wu ◽  
Cheng Liu
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Financial Distress ◽  
Clustering Algorithm ◽  
Rough Set Theory ◽  
Financial Indicators ◽  
Financial Status ◽  
Level Data ◽  
Research Objects

The traditional financial distress method normally divided samples into two categories by healthy and bankruptcy. And the financial indicators are typically chosen without using a systematic and reasonable theory. To be more realistic, this paper selected all the companies in a certain industry as the research objects. Twenty-one financial indicators were primarily chosen as the condition attributes, reduction set was obtained by matrix reduction identification based on rough set theory. Then PSO-based clustering algorithm K-means was used to divide subjects into 5 categories of different financial status. The decision-making table was formed with the reduction set using the classification as a decision attribute. Finally, we tested the reasonableness of the classification and generated early warning rules together with rough set theory to evaluate the financial status of listed companies. The results showed that PSO-based K-means algorithm was able to reasonably classify companies, at the same time to overcome the subjective impacts in the artificial measure of financial crisis level. Data generated using this method agreed with the rough set theory for up to 87.0%, thus proving this method to be effective and feasible.

scholarly journals IDENTIFYING KEY FACTORS FOR ADOPTING ARTIFICIAL INTELLIGENCE-ENABLED AUDITING TECHNIQUES BY JOINT UTILIZATION OF FUZZY-ROUGH SET THEORY AND MRDM TECHNIQUE

2020 ◽  
Vol 0 (0) ◽  
pp. 1-34
Author(s):  
Kuang-Hua Hu ◽  
Fu-Hsiang Chen ◽  
Ming-Fu Hsu ◽  
Gwo-Hshiung Tzeng
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Audit Quality ◽  
Rough Set Theory ◽  
Decision Makers ◽  
Fuzzy Rough Set ◽  
Rule Based ◽  
Cpa Firms

In today’s big-data era, enterprises are able to generate complex and non-structured information that could cause considerable challenges for CPA firms in data analysis and to issue improper audited reports within the required period. Artificial intelligence (AI)-enabled auditing technology not only facilitates accurate and comprehensive auditing for CPA firms, but is also a major breakthrough in auditing’s new environment. Applications of an AI-enabled auditing technique in external auditing can add to auditing efficiency, increase financial reporting accountability, ensure audit quality, and assist decision-makers in making reliable decisions. Strategies related to the adoption of an AI-enabled auditing technique by CPA firms cover the classical multiple criteria decision-making (MCDM) task (i.e., several perspectives/criteria must be considered). To address this critical task, the present study proposes a fusion multiple rule-based decision making (MRDM) model that integrates rule-based technique (i.e., the fuzzy rough set theory (FRST) with ant colony optimization (ACO)) into MCDM techniques that can assist decision makers in selecting the best methods necessary to achieve the aspired goals of audit success. We also consider potential implications for articulating suitable strategies that can improve the adoption of AI-enabled auditing techniques and that target continuous improvement and sustainable development.

scholarly journals Decision Making Using Rough Topology and Indiscernibility Matrix for Corona Virus Diagnosis

2020 ◽  
pp. 31-33
Author(s):  
Kanchana. M ◽  
Rekha. S
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Mathematical Tool ◽  
Virus Diagnosis ◽  
Corona Virus ◽  
Uncertain Knowledge

Rough set theory is a new mathematical tool for dealing with vague, imprecise, inconsistent and uncertain knowledge. In recent years the research and applications on rough set theory have attracted more. In this paper, we have introduced and analyze the Rough set theory and also decide the factors for corona virus diagnosis by using Indiscernibility matrix.

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