A survey on rough set theory and its applications
01 Oct 2016-CAAI Transactions on Intelligence Technology (No longer published by Elsevier)-Vol. 1, Iss: 4, pp 323-333
TL;DR: The basic concepts, operations and characteristics on the rough set theory are introduced, and then the extensions of rough set model, the situation of their applications, some application software and the key problems in applied research for the roughSet theory are presented.
About: This article is published in CAAI Transactions on Intelligence Technology.The article was published on 2016-10-01 and is currently open access. It has received 185 citations till now. The article focuses on the topics: Dominance-based rough set approach & Rough set.
Citations
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TL;DR: It is shown that by integrating the rough approach with the traditional fuzzy approach, the subjectivity that exists when defining the borders of fuzzy sets is eliminated.
Abstract: This paper presents a new approach for the treatment of uncertainty which is based on interval-valued fuzzy-rough numbers (IVFRN). It is shown that by integrating the rough approach with the traditional fuzzy approach, the subjectivity that exists when defining the borders of fuzzy sets is eliminated. IVFRN make decision making possible using only the internal knowledge in the operative data available to the decision makers. In this way objective uncertainties are used and there is no need to rely on models of assumptions. Instead of different external parameters in the application of IVFRN, the structure of the given data is used. On this basis an original multi-criteria model was developed based on an IVFRN approach. In this multi-criteria model the traditional steps of the BWM (Best–Worst method) and MABAC (Multi-Attributive Border Approximation area Comparison) methods are modified. The model was tested and validated on a study of the optimal selection of fire fighting helicopters. Testing demonstrated that the model based on IVFRN enabled more objective expert evaluation of the criteria in comparison with traditional fuzzy and rough approaches. A sensitivity analysis of the IVFRN BWM-MABAC model was carried out by means of 57 scenarios, the results of which showed a high degree of stability. The results of the IVFRN model were validated by comparing them with the results of the fuzzy and rough extension of the MABAC, COPRAS and VIKOR models.
243 citations
TL;DR: In this paper, the authors presented a reliable GIS multi-criteria methodology for hazard zones' mapping of flood-prone areas in urban areas based on the combined application of geographical information systems (GIS) and multicriteria decision analysis (MCDA).
Abstract: Floods are natural disasters with significant socio-economic consequences Urban areas with uncontrolled urban development, rapid population growth, an unregulated municipal system and an unplanned change of land use belong to the highly sensitive areas where floods cause devastating economic and social losses The aim of this paper is to present a reliable GIS multi-criteria methodology for hazard zones’ mapping of flood-prone areas in urban areas The proposed methodology is based on the combined application of geographical information systems (GIS) and multi-criteria decision analysis (MCDA) The methodology considers six factors that are relevant to the hazard of flooding in urban areas: the height, slope, distance to the sewage network, the distance from the water surface, the water table and land use The expert evaluation takes into account the nature and severity of observed criteria, and it is tested using three scenarios: the modalities of the analytic hierarchy process (AHP) The first of them uses a new approach to the exploitation of uncertainty in the application of the AHP technique, the interval rough numbers (IR’AHP) The second one uses the fuzzy technique for the exploitation of uncertainty with the AHP method (F’AHP), and the third scenario contemplates the use of the traditional (crisp) AHP method The proposed methodology is demonstrated in Palilula Municipality, Belgrade, Serbia In the last few decades, Palilula Municipality has been repeatedly devastated by extreme flood events These floods severely affected the transportation networks and other infrastructure Historical flood inundation data have been used in the validation process The final urban flood hazard map proves a satisfactory agreement between the flood hazard zones and the spatial distribution of historical floods that happened in the last 58 years The results indicate that the scenario in which the IR’AHP methodology is used provides the highest level of compatibility with historical data on floods The produced map showed that the areas of very high flood hazard are located on the left Danube River bank These areas are characterized by lowland morphology, gentle slope, sewage network, expansion of impermeable locations and intense urbanization The proposed GIS-IR’AHP methodology and the results of this study provide a good basis for developing a system of flood hazard management in urban areas and can be successfully used for spatial city development policy
187 citations
TL;DR: An overview of MULTIMOORA is conducted by categorizing and analyzing main researches, theoretically and practically, in terms of the subordinate ranking methods, ranking aggregation tools, weighting methods, group decision-making, combination with other models, and the robustness of the method.
155 citations
TL;DR: A hybrid multi-criteria model was developed based on IRN, and demonstrated using the example of the bidder selection process in the state administration public procurement procedure, which showed high stability of the results.
Abstract: Interval rough number is introduced to deal with the vagueness in decision-making.A novel DEMATEL-ANP model based oninterval rough numbers.Application of a new multi-criteria technique called MARICA.An interval rough number based on MARICA is proposed to evaluate the alternatives.Multi-criteria techniques were compared based on interval rough and fuzzy approaches. This paper presents a novel approach for treating uncertainty in the multi-criteria decision making process by introducing interval rough numbers (IRN). The IRN approach enables decision making using only the internal knowledge incorporated in the data provided by the decision maker. A hybrid multi-criteria model was developed based on IRN, and demonstrated using the example of the bidder selection process in the state administration public procurement procedure. The first segment of the hybrid model deals with the rough interval DEMATEL-ANP (IR'DANP) model, which enables more objective expert evaluation of criteria in a subjective environment than the traditional/crisp approach. In the second segment, the evaluation is enabled by applying the new rough interval MAIRCA method, which introduces mathematical tools and shows high stability concerning changes in the nature and characteristics of the criteria. The results of the hybrid IR'DANP-MAIRCA model were analyzed using 36 scenarios of sensitivity analysis, which showed high stability of the results. The results of the interval rough method were compared with the fuzzy extensions of the TOPSIS, VIKOR, MABAC, TODIM, ELECTRE I and DEMATEL-ANP models.
148 citations
TL;DR: This article employs a systematic literature survey approach to systematically review statistical and machine learning models in credit scoring, to identify limitations in literature, to propose a guiding machine learning framework, and to point to emerging directions.
141 citations
References
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TL;DR: The basic concepts of rough set theory are presented and some rough set-based research directions and applications are pointed out, indicating that the rough set approach is fundamentally important in artificial intelligence and cognitive sciences.
2,004 citations
TL;DR: A generalized model of rough sets called variable precision model (VP-model), aimed at modelling classification problems involving uncertain or imprecise information, is presented and the main concepts are introduced formally and illustrated with simple examples.
1,975 citations
TL;DR: This work proposes reduction of knowledge that eliminates only that information, which is not essential from the point of view of classification or decision making, and shows how to find decision rules directly from such an incomplete decision table.
1,239 citations
TL;DR: The algorithm for feature selection is based on an application of a rough set method to the result of principal components analysis (PCA) used for feature projection and reduction.
801 citations
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