scholarly journals Rough set theory in the classification of loan applications

2020 ◽  
Vol 176 ◽  
pp. 3235-3244
Author(s):  
Jarosław Becker ◽  
Aleksandra Radomska-Zalas ◽  
Paweł Ziemba
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory

Rough Set Theory

2011 ◽  
pp. 783-789
Author(s):  
Malcolm J. Beynon
Keyword(s):  
Decision Support ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Customer Relationship ◽  
Classification Problems ◽  
Variable Precision ◽  
Variable Precision Rough Sets

Rough set theory (RST), since its introduction in Pawlak (1982), continues to develop as an effective tool in classification problems and decision support. In the majority of applications using RST based methodologies, there is the construction of ‘if .. then ..’ decision rules that are used to describe the results from an analysis. The variation of applications in management and decision making, using RST, recently includes discovering the operating rules of a Sicilian irrigation purpose reservoir (Barbagallo, Consoli, Pappalardo, Greco, & Zimbone, 2006), feature selection in customer relationship management (Tseng & Huang, 2007) and decisions that insurance companies make to satisfy customers’ needs (Shyng, Wang, Tzeng, & Wu, 2007). As a nascent symbolic machine learning technique, the popularity of RST is a direct consequence of its set theoretical operational processes, mitigating inhibiting issues associated with traditional techniques, such as within-group probability distribution assumptions (Beynon & Peel, 2001). Instead, the rudiments of the original RST are based on an indiscernibility relation, whereby objects are grouped into certain equivalence classes and inference taken from these groups. Characteristics like this mean that decision support will be built upon the underlying RST philosophy of “Let the data speak for itself” (Dunstch & Gediga, 1997). Recently, RST was viewed as being of fundamental importance in artificial intelligence and cognitive sciences, including decision analysis and decision support systems (Tseng & Huang, 2007). One of the first developments on RST was through the variable precision rough sets model (VPRSß), which allows a level of mis-classification to exist in the classification of objects, resulting in probabilistic rules (see Ziarko, 1993; Beynon, 2001; Li and Wang, 2004). VPRSß has specifically been applied as a potential decision support system with the UK Monopolies and Mergers Commission (Beynon & Driffield, 2005), predicting bank credit ratings (Griffiths & Beynon, 2005) and diffusion of medicaid home care programs (Kitchener, Beynon, & Harrington, 2004). Further developments of RST include extended variable precision rough sets (VPRSl,u), which infers asymmetric bounds on the possible classification and mis-classification of objects (Katzberg & Ziarko, 1996), dominance-based rough sets, which bases their approach around a dominance relation (Greco, Matarazzo, & Slowinski, 2004), fuzzy rough sets, which allows the grade of membership of objects to constructed sets (Greco, Inuiguchi, & Slowinski, 2006), and probabilistic bayesian rough sets model that considers an appropriate certainty gain function (Ziarko, 2005). A literal presentation of the diversity of work on RST can be viewed in the annual volumes of the Transactions on Rough Sets (most recent year 2006), also the annual conferences dedicated to RST and its developments (see for example, RSCTC, 2004). In this article, the theory underlying VPRSl,u is described, with its special case of VPRSß used in an example analysis. The utilisation of VPRSl,u, and VPRSß, is without loss of generality to other developments such as those referenced, its relative simplicity allows the non-proficient reader the opportunity to fully follow the details presented.

ABC analysis of the customers using axiomatic design and incomplete rough set

2018 ◽  
Vol 52 (4-5) ◽  
pp. 1219-1232 ◽  
Author(s):  
Atena Gholami ◽  
Reza Sheikh ◽  
Neda Mizani ◽  
Shib Sankar Sana
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Success Factors ◽  
Rough Set Theory ◽  
Axiomatic Design ◽  
Target Market ◽  
Common Occurrence ◽  
Abc Classification ◽  
Abc Analysis

Customer’s recognition, classification, and selecting the target market are the most important success factors of a marketing system. ABC classification of the customers based on axiomatic design exposes the behavior of the customer in a logical way in each class. Quite often, missing data is a common occurrence and can have a significant effect on the decision- making problems. In this context, this proposed article determines the customer’s behavioral rule by incomplete rough set theory. Based on the proposed axiomatic design, the managers of a firm can map the rules on designed structures. This study demonstrates to identify the customers, determine their characteristics, and facilitate the development of a marketing strategy.

A data mining framework for classification of organisational performance based on rough set theory

2018 ◽  
Vol 3 (2) ◽  
pp. 156
Author(s):  
Danial Rezaei ◽  
Mohsen Maleki ◽  
Hamid Hasani ◽  
Seyed Mohammad Jafar Jalali
Keyword(s):  
Data Mining ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Organisational Performance

Enhanced classification of LISS-III satellite image using rough set theory and ANN

2019 ◽  
Vol 8 (3) ◽  
pp. 249
Author(s):  
Anand Upadhyay ◽  
Jyotsna Anthal ◽  
Shashank Shukla
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Satellite Image ◽  
Liss Iii

The Sensitivity of Water Environmental Factors on Eutrophication Based on Rough Set Theory and the Ecological Countermeasures Revelation

2012 ◽  
Vol 531 ◽  
pp. 446-450
Author(s):  
Xian Xiang Luo ◽  
Juan Zhang ◽  
Long Jun Zhang
Keyword(s):  
Environmental Factors ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
River Estuary ◽  
Laizhou Bay ◽  
Key Factors ◽  
Ecological Measurement ◽  
Xiaoqing River Estuary

The paper took advantage of approximate classification of imprecise or incomplete data to analyze and assess the sensitivity of environmental factors on eutrophication based on rough set theory. Its main advantage was it performed rationally on data attributes reduction and classification. This technique provided a valuable tool to analyze the key factors leading to eutrophication in Xiaoqing River estuary of Laizhou Bay in China. Results showed that the most important factors that dominated the eutrophic degree were aerobiotic organic compound, dissolved inorganic phosphate and nitrogen which came from the land-input of Xiaoqing River. Then the ecological measurement against eutrophication may be to cultivate a kind of ecological environment materials (e.g. seaweed) to absorb the excessive nutrition.

Attribute clustering using rough set theory for feature selection in fault severity classification of rotating machinery

2017 ◽  
Vol 71 ◽  
pp. 69-86 ◽  
Author(s):  
Fannia Pacheco ◽  
Mariela Cerrada ◽  
René-Vinicio Sánchez ◽  
Diego Cabrera ◽  
Chuan Li ◽  
...  
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Rotating Machinery ◽  
Severity Classification ◽  
Fault Severity ◽  
Attribute Clustering

scholarly journals Classification of Volcanic Rocks based on Rough Set Theory

2020 ◽  
Vol 10 (2) ◽  
pp. 5501-5504
Author(s):  
S. M. Shaaban ◽  
S. Z. Tawfik
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Volcanic Rocks ◽  
Information Loss ◽  
Novel Approach ◽  
Chemical Content ◽  
Rule Sets ◽  
Loss Method

Classification of volcanic rocks is a fundamental task in the geologic studies. Volcanic rocks are igneous rocks that cooled rapidly above the surface of the Earth's crust. They are classified according to their oxide chemical content. Furthermore, volcanic rocks can also be classified numerically by statistical means. But these methods are mostly dependent on human expert decision making and have a high cost. In this paper, a novel approach in the classification of volcanic rocks is proposed. This method is based on the rough set mathematical theory. The continuous data of the information system are firstly discretized using the information loss method. Secondly, the discretized decision table is reduced and the decision rule sets are extracted. The results are consistent with previous methods and show that the proposed method reduces time and calculation costs.

Facial Expression Recognition Based on RS-SVM

2014 ◽  
Vol 543-547 ◽  
pp. 2329-2332
Author(s):  
Dong Li
Keyword(s):  
Facial Expression ◽  
Set Theory ◽  
Rough Set ◽  
Facial Expression Recognition ◽  
Rough Set Theory ◽  
Rapid Development ◽  
Classification Performance ◽  
Svm Classifier ◽  
Expression Recognition

In Recent years, with the rapid development of facial expression recognition technology, processing and classification of facial expression recognition has become a hotspot in application studies of remote sensing. Rough set theory (RS) and SVM have unique advantages in information processing and classification. This paper applies RS-SVM to facial expression recognition, briefly introduce the concepts of RS and principle of SVM, attributes reduction in RS theory as preposing system to get rid of redundancy attributes. Meanwhile, the SVM classifier works as postposing system helps training and classifying the facial expression recognition. Experimental results indicate this model not only raise the operating speed, but also improve classification performance, providing a new effective way in facial expression recognition technology.

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