Comparing and Contrasting Rough Set with Logistic Regression for a Dataset

2014 ◽  
Vol 1 (1) ◽  
pp. 81-98 ◽  
Author(s):  
Renu Vashist ◽  
M. L. Garg
Keyword(s):  
Decision Making ◽  
Logistic Regression ◽  
Rough Set ◽  
Rough Set Theory ◽  
Mathematical Tool ◽  
Data Set ◽  
Imperfect Data ◽  
Logit Regression ◽  
Data Value ◽  
Wide Range

Rough Set Theory (RST) is relatively new and powerful mathematical tool to deal with imperfect data (i.e. data with uncertainty and vagueness) which is primarily used for classification and decision making problems. On the other hand, Logistic regression (Logit) is mainly used in Social Sciences when dependent variable takes limited and categorical data value ranges. However, both RST and Logit regression are powerful predictable models that are used in wide range of applications such as medicine, military, banking, financial markets etc. RST uses approximations and implications as two formal tools to deal with vagueness whereas Logit regression is severely constrained to deal with vague and imprecise data. Yet, both these methodologies are used to classify the object which is the key issue in decision making. This research paper compares these two tools on a common dataset. SPSS 17.0 software is used to run the Logit regression and Rose 2 software is used for analysis of Rough Set. One of the important finding of this comparison is that attributes in core of the data set under the rough set approach are similar to the most significant predictors of logistic regression model. This indicates that the significant attributes deducted by these two methodologies are similar. It is demonstrated that rough set is much more superior tool to classify the objects as compared to logistic regression. One of the important outcomes of this research is that degree of accuracy is much higher in rough set as compared to logistic regression thereby establishing the supremacy of rough set as a better decision making tool.

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.

The relative decision-making algorithm for ranking data

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yin-Ju Chen ◽  
Jian-Ming Lo
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Design Methodology ◽  
Rough Set Theory ◽  
Decision Makers ◽  
Data Sets ◽  
Ranking Data ◽  
Data Set ◽  
Content Type ◽  
Relative Concept

PurposeDecision-making is always an issue that managers have to deal with. Keenly observing to different preferences of the targets provides useful information for decision-makers who do not require too much information to make decisions. The main purpose is to avoid decision-makers in a dilemma because of too much or opaque information. Based on problem-oriented, this research aims to help decision-makers to develop a macro-vision strategy that fits the needs of different clusters of customers in terms of their favorite restaurants. This research also focuses on providing the rules to rank data sets for decision-makers to make choices for their favorite restaurant.Design/methodology/approachWhen the decision-makers need to rethink a new strategic planning, they have to think about whether they want to retain or rebuild their relationship with the old consumers or continue to care for new customers. Furthermore, many of the lecturers show that the relative concept will be more effective than the absolute one. Therefore, based on rough set theory, this research proposes an algorithm of related concepts and sends questionnaires to verify the efficiency of the algorithm.FindingsBy feeding the relative order of calculating the ranking rules, we find that it will be more efficient to deal with the faced problems.Originality/valueThe algorithm proposed in this research is applied to the ranking data of food. This research proves that the algorithm is practical and has the potential to reveal important patterns in the data set.

THE INFORMATION ENTROPY, ROUGH ENTROPY AND KNOWLEDGE GRANULATION IN ROUGH SET THEORY

2004 ◽  
Vol 12 (01) ◽  
pp. 37-46 ◽  
Author(s):  
JIYE LIANG ◽  
ZHONGZHI SHI
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Information Entropy ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Computer Applications ◽  
Mathematical Tool

Rough set theory is a relatively new mathematical tool for use in computer applications in circumstances which are characterized by vagueness and uncertainty. In this paper, we introduce the concepts of information entropy, rough entropy and knowledge granulation in rough set theory, and establish the relationships among those concepts. These results will be very helpful for understanding the essence of concept approximation and establishing granular computing in rough set theory.

Research on the Consumer Demand of Decision Making in the E-Commence System Building Process

2011 ◽  
Vol 71-78 ◽  
pp. 2895-2898
Author(s):  
Jian Min Xie ◽  
Qin Qin
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Consumer Satisfaction ◽  
Rough Set Theory ◽  
Consumer Demand ◽  
Successful Implementation ◽  
Business Decision ◽  
Building Process ◽  
Demand Reduction ◽  
The Impact

In the process of developing e-commerce system, enterprises are able to accurately understand and grasp the needs of the user enterprise that is the key to the successful implementation of e-commerce. Based on this, the article proposed a new method which was based on the process of developing consumer demand for e-business decision-making though a rough set theory. This new approach is built using rough set decision model to calculate the different needs of the impact on consumer satisfaction, come to an important degree of each demand and the demand reduction order. This method overcomes the traditional rough set method cumbersome bottlenecks, and helps operating; cases studies show that the proposed method is simple and effective.

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.

Combination with Machine Learning Algorithms for the Classification in E-Bussiness

2011 ◽  
Vol 230-232 ◽  
pp. 625-628
Author(s):  
Lei Shi ◽  
Xin Ming Ma ◽  
Xiao Hong Hu
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Mathematical Tool ◽  
Vector Machines

E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

scholarly journals An Automated Implementation of Academic Staff Performance Evaluation System based on Rough Sets Theory

2019 ◽  
Vol 23 ◽  
Author(s):  
Bolanle Ojokoh ◽  
Victor Akinsulire ◽  
Folasade O Isinkaye
Keyword(s):  
Performance Evaluation ◽  
Human Resource ◽  
Rough Set ◽  
Evaluation System ◽  
Rough Set Theory ◽  
Academic Staff ◽  
Evaluation Procedure ◽  
Rough Sets Theory ◽  
Imperfect Data ◽  
Automated Method

The essence of evaluating employees’ performance in any tertiary institution is to realize the goals of the institution by measuring the contribution of each employee. Effective human resource evaluation is paramount to the development of any organization. An automated method is needed to remove the limitations and facilitate the duties of human resource management. In this paper, rough set theory, a mathematical technique that deals with vagueness and uncertainty of imperfect data analysis is adopted for the evaluation of academic staff profile for promotion, grants and other academic purposes. The entire appraisal process of academic staff was translated into a web-based application where every user can fill, edit, update, and submit the annual performance evaluation report form. The indiscernible property of rough set approach is a unique factor in assessing every academic staff under the department and faculty/school by the head of department and dean respectively. With this, the system generates an information table handling all the necessary conditions for promoting academic staff and the corresponding decisions taken. A model for rating publications was proposed to reduce the sentiments involved in manual rating. Reports were generated as output of each evaluation procedure. One hundred (100) dataset of academic staff of the Federal University of Technology, Akure, Nigeria was used in the experiment to evaluate the performance of the system. The results of the system obtained score were compared with the institution standard and it was found that the system scores were above standard, the average precision of the system shows 60% effectiveness which showed that the proposed system is efficient for academic performance evaluation process.

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