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Article
Publication date: 28 October 2014

Yong Liu, Wu-yong Qian and Jeffrey Forrest

– The purpose of this paper is to construct a novel grey dominance variable precision rough model.

Abstract

Purpose

The purpose of this paper is to construct a novel grey dominance variable precision rough model.

Design/methodology/approach

To deal with the problems that the attribute values of the decision-making object are often not exact numbers but interval grey numbers, and the decision-making attributes satisfy a certain preference relationship in the decision-making information because of the complexity and uncertainty of the real world, the authors take advantage of the theoretical thinking of the grey systems, dominance rough set theory and variable precision rough set theory, and construct a novel dominance variable precision rough set model. On the basis of the thinking logic of grey systems, the authors first define the concepts of balance degree, dominance degree and inferior degree, and then the grey dominance relationship based on the comparison of interval grey numbers. Then the authors use the grey dominance relationship to substitute for the indiscernibility relationship of the variable precision rough set so that the grey dominance variable precision rough model is naturally utilized to reduce the system's attributes in order to derive the needed decision rules. At the end, the authors use a decision-making example of the radar target selection to demonstrate the feasibility and effectiveness of the novel model.

Findings

The results show that the proposed model possesses certain fault tolerance ability and can well-realize decision rule extraction and knowledge discovery out of a given incomplete information system.

Practical implications

The method exposed in the paper can be used to deal with the decision-making problems with the grey information, preference information and noise data.

Originality/value

The paper succeeds in realizing both the grey decision-making information with preference information and noise data and the extraction of decision-making rules.

Details

Grey Systems: Theory and Application, vol. 4 no. 3
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 1 December 2003

Nii O. Attoh‐Okine

This paper describes the application of rough set theory to equipment productivity estimate. Rough set theory offers a novel approach both in generation of rules and statistical…

540

Abstract

This paper describes the application of rough set theory to equipment productivity estimate. Rough set theory offers a novel approach both in generation of rules and statistical classification of the equipment productivity data. The approach is based on upper and lower approximations of a set in terms of positive, negative and boundary regions. The information about the equipment productivity is organized in productivity information table. The table contains data about the object of interest characterized in terms of some attributes. The paper is more of an exploratory research to determine how rough set theory can be applicable to specific construction engineering problems.

Details

Engineering, Construction and Architectural Management, vol. 10 no. 6
Type: Research Article
ISSN: 0969-9988

Keywords

Article
Publication date: 1 August 2002

Maurizio d’Amato

This research is focused on a methodology created to analyse imprecise information, that is full of attributes defined as “rough set”. The methodology will be then applied to the…

Abstract

This research is focused on a methodology created to analyse imprecise information, that is full of attributes defined as “rough set”. The methodology will be then applied to the real estate appraisal question, representing a further possible method of evaluation. Up to now the main approaches to the real estate appraisal have been income, market and cost. My intention is to analyse this theory showing a practical application on a group of real estate transactions made by a real estate agent. This application will show how it is possible to get values to classify a real estate. A comparison between this method and the most common statistics instruments will be highlighted.

Details

Journal of Property Investment & Finance, vol. 20 no. 4
Type: Research Article
ISSN: 1463-578X

Keywords

Article
Publication date: 11 May 2023

Arpit Singh, Vimal Kumar and Pratima Verma

This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough…

Abstract

Purpose

This study aims to focus on sustainable supplier selection in a construction company considering a new multi-criteria decision-making (MCDM) method based on dominance-based rough set analysis. The inclusion of sustainability concept in industrial supply chains has started gaining momentum due to increased environmental protection awareness and social obligations. The selection of sustainable suppliers marks the first step toward accomplishing this objective. The problem of selecting the right suppliers fulfilling the sustainable requirements is a major MCDM problem since various conflicting factors are underplay in the selection process. The decision-makers are often confronted with inconsistent situations forcing them to make imprecise and vague decisions.

Design/methodology/approach

This paper presents a new method based on dominance-based rough sets for the selection of right suppliers based on sustainable performance criteria relying on the triple bottom line approach. The method applied has its distinct advantages by providing more transparency in dealing with the preference information provided by the decision-makers and is thus found to be more intuitive and appealing as a performance measurement tool.

Findings

The technique is easy to apply using “jrank” software package and devises results in the form of decision rules and ranking that further assist the decision-makers in making an informed decision that increases credibility in the decision-making process.

Originality/value

The novelty of this study of its kind is that uses the dominance-based rough set approach for a sustainable supplier selection process.

Details

Construction Innovation , vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1471-4175

Keywords

Article
Publication date: 1 December 2002

Peter Nijkamp and Gabriella Vindigni

This paper offers an overview of factors that are decisive for productivity increase in the agricultural sector (both farming and agro‐food). An attempt is made to explain…

2436

Abstract

This paper offers an overview of factors that are decisive for productivity increase in the agricultural sector (both farming and agro‐food). An attempt is made to explain differences in total factor productivity in agriculture in different countries by means of meta‐analysis, in particular, by using rough set theory as a framework for comparative study. The main aim is to derive the drivers of changes in agricultural food production with a view to conditional future predictions of an “if … then” nature. The empirical application to OECD countries is used to illustrate the potential of this new approach for identifying critical success factors in agriculture with a view to future food security objectives.

Details

Environmental Management and Health, vol. 13 no. 5
Type: Research Article
ISSN: 0956-6163

Keywords

Article
Publication date: 24 August 2010

Christopher Henry and James F. Peters

The purpose of this paper is to present near set theory using the perceptual indiscernibility and tolerance relations, to demonstrate the practical application of near set theory…

3294

Abstract

Purpose

The purpose of this paper is to present near set theory using the perceptual indiscernibility and tolerance relations, to demonstrate the practical application of near set theory to the image correspondence problem, and to compare this method with existing image similarity measures.

Design/methodology/approach

Image‐correspondence methodologies are present in many systems that are depended on daily. In these systems, the discovery of sets of similar objects (aka, tolerance classes) stems from human perception of the objects being classified. This view of perception of image‐correspondence springs directly from Poincaré's work on visual spaces during 1890s and Zeeman's work on tolerance spaces and visual acuity during 1960s. Thus, in solving the image‐correspondence problem, it is important to have systems that accurately model human perception. Near set theory provides a framework for measuring the similarity of digital images (and perceptual objects, in general) based on features that describe them in much the same way that humans perceive objects.

Findings

The contribution of this paper is a perception‐based classification of images using near sets.

Originality/value

The method presented in this paper represents a new approach to solving problems in which the goal is to match human perceptual groupings. While the results presented in the paper are based on measuring the resemblance between images, the approach can be applied to any application that can be formulated in terms of sets such that the objects in the sets can be described by feature vectors.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 3 no. 3
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 9 January 2009

Wei‐Shing Chen

This paper seeks to present the use of Rough Sets (RS) theory as a processing method to improve the results in customer satisfaction survey applications.

2913

Abstract

Purpose

This paper seeks to present the use of Rough Sets (RS) theory as a processing method to improve the results in customer satisfaction survey applications.

Design/methodology/approach

The research methodology is to apply an innovative tool to discover knowledge on customer behavior patterns instead of using conventional statistical methods. The RS theory was applied to discover the voice of customers in market research. The collected data contained 422 records. Each record included 20 condition attributes as well as two decision attributes. The important attributes that ensured high quality of classification were generated first. Then decision rules for classifying high and low overall satisfaction and loyalty categories were derived.

Findings

Three important facts were found: the important product and service attributes that lead to overall satisfaction and loyalty; the percentage of latently dissatisfied customers; and customer decision rules.

Research limitations/implications

The study is limited by the case company and its experience. These rules were presented to the company's sales and marketing managers who believed that they provided them with valuable information for creating strategies to increase customer satisfaction and retention.

Originality/value

RS theory provides a mathematical tool to discover patterns hidden in survey data. The paper describes a new attempt of applying a RS‐based method to analyze overall customer satisfaction and loyalty behavior through regular satisfaction questionnaire surveys.

Details

Asia Pacific Journal of Marketing and Logistics, vol. 21 no. 1
Type: Research Article
ISSN: 1355-5855

Keywords

Article
Publication date: 1 September 2004

Angel M. Gento

In the present time, there are large databases with parameters related to the maintenance of different equipment and installations. Given that manual analysis of sensors connected…

1380

Abstract

In the present time, there are large databases with parameters related to the maintenance of different equipment and installations. Given that manual analysis of sensors connected to machines is practically impossible, maintenance decisions from these databases can be difficult if the information automatically updated from these sensors is huge. Those great amounts of information are essentially useless if the knowledge contained inside cannot be extracted. Rough set theory facilitates this work by detecting those parameters that are truly significant for establishing the decision rules of the maintenance. In order to show the power of rough sets this paper contains a real case of a plastic injection installation for the analysis. Practical implications. An effective use of resource allocation in manufacturing processes could be achieved by using certain decision rules to indicate where and when maintenance decisions and tasks should be undertaken. This paper illustrates how the powerful theory of rough sets handles these issues. Therefore, the use of this technique is highly recommended for those industrial processes with a great amount of data and time (or in general, any resource) limitations.

Details

Journal of Quality in Maintenance Engineering, vol. 10 no. 3
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 5 May 2021

Samrat Gupta and Swanand Deodhar

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is…

Abstract

Purpose

Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

Design/methodology/approach

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Findings

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

Research limitations/implications

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

Practical implications

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

Social implications

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

Originality/value

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

Details

Information Technology & People, vol. 37 no. 2
Type: Research Article
ISSN: 0959-3845

Keywords

Open Access
Article
Publication date: 29 November 2019

Kai Yu, Liqun Peng, Xue Ding, Fan Zhang and Minrui Chen

Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and…

1312

Abstract

Purpose

Basic safety message (BSM) is a core subset of standard protocols for connected vehicle system to transmit related safety information via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). Although some safety prototypes of connected vehicle have been proposed with effective strategies, few of them are fully evaluated in terms of the significance of BSM messages on performance of safety applications when in emergency.

Design/methodology/approach

To address this problem, a data fusion method is proposed to capture the vehicle crash risk by extracting critical information from raw BSMs data, such as driver volition, vehicle speed, hard accelerations and braking. Thereafter, a classification model based on information-entropy and variable precision rough set (VPRS) is used for assessing the instantaneous driving safety by fusing the BSMs data from field test, and predicting the vehicle crash risk level with the driver emergency maneuvers in the next short term.

Findings

The findings and implications are discussed for developing an improved warning and driving assistant system by using BSMs messages.

Originality/value

The findings of this study are relevant to incorporation of alerts, warnings and control assists in V2V applications of connected vehicles. Such applications can help drivers identify situations where surrounding drivers are volatile, and they may avoid dangers by taking defensive actions.

Details

Journal of Intelligent and Connected Vehicles, vol. 2 no. 2
Type: Research Article
ISSN: 2399-9802

Keywords

1 – 10 of 39