DATA BALANCING METHODS BY FUZZY ROUGH SETS

2020 ◽  
pp. 40-59
Author(s):  
Tran Thanh Huyen
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Binary Classification ◽  
Imbalanced Data ◽  
Fuzzy Rough Set ◽  
Rough Sets Theory ◽  
Time Consumption ◽  
Performance Accuracy ◽  
Complete Study ◽  
Sets Theory

The robustness of rough sets theory in data cleansing have been proved in many studies. Recently, fuzzy rough set also make a deal with imbalanced data by two approaches. The first is a combination of fuzzy rough instance selection and balancing methods. The second tries to use different criteria to clean majorities and minorities classes of imbalanced data. This work is an extension of the second method which was presented in [16]. The paper depicts complete study about the second method with some proposed algorithms. It focuses mainly on binary classification with kNN and SVM for imbalanced data. Experiments and comparisons among related methods will confirm pros and coin of each method with respect to performance accuracy and time consumption.

Rough Sets Based Prediction Model of Tick-Wise Price Fluctuations

2011 ◽  
Vol 15 (4) ◽  
pp. 449-453 ◽  
Author(s):  
Yoshiyuki Matsumoto ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Time Series ◽  
Decision Rule ◽  
Rough Set ◽  
Rough Sets ◽  
Time Series Data ◽  
Series Data ◽  
Data Set ◽  
Price Fluctuations ◽  
Rough Sets Theory ◽  
Sets Theory

Rough sets theory was proposed by Z. Pawlak in 1982. This theory enables us to mine knowledge granules through a decision rule from a database, a web base, a set and so on. We can apply the decision rule to reason, estimate, evaluate, or forecast unknown objects. In this paper, the rough set model is used to analyze of time series data of tick-wise price fluctuation, where knowledge granules are mined from the data set of tick-wise price fluctuations.

Feature Extraction from Engine's Data Based on Rough Sets Theory

2011 ◽  
Vol 120 ◽  
pp. 410-413
Author(s):  
Feng Wang ◽  
Li Xin Jia
Keyword(s):  
Feature Extraction ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Technical Condition ◽  
Decision Table ◽  
Discernibility Matrix ◽  
Rough Sets Theory ◽  
Sets Theory

The speed signal of engine contains abundant information. This paper introduces rough set theory for feature extraction from engine's speed signals, and proposes a method of mining useful information from a mass of data. The result shows that the discernibility matrix algorithm can be used to reduce attributes in decision table and eliminate unnecessary attributes, efficiently extracted the features for evaluating the technical condition of engine.

Application of Rough Sets Theory in Graduate Enrollment

2014 ◽  
Vol 687-691 ◽  
pp. 1604-1607
Author(s):  
Juan Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Postgraduate Training ◽  
Rough Set Theory ◽  
Postgraduate Education ◽  
Rough Sets Theory ◽  
Graduate Enrollment ◽  
Enrollment Data ◽  
Sets Theory

Enrollment is the first step of the work of postgraduate education, and also a very crucial step. Therefore, in order to successfully carry out the postgraduate training work, be sure to do the enrollment work. In this paper, rough set theory is applied to the enrollment data of one college of a university. Then follow the general steps of data mining to research and analyze the enrollment data. Finally, draw some useful conclusions. These conclusions have a certain significance for graduate enrollment.

Application of MMAS and Rough Sets in Compound-Fault Diagnosis of Bearing

2012 ◽  
Vol 433-440 ◽  
pp. 6319-6324 ◽  
Author(s):  
Hai Ying Kang ◽  
Ren Fa Shen ◽  
Yan Jie Qi ◽  
Wen Yan ◽  
Hai Qi Zheng
Keyword(s):  
Fault Diagnosis ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Ant System ◽  
Fisher Criterion ◽  
Rough Sets Theory ◽  
Np Hard Problem ◽  
Sets Theory

The diagnosis of compound-fault is always a difficult point, and there is not an effective method in equipment diagnosis field. Rough set theory is a relatively new soft computing tool to deal with vagueness and uncertainty. Condition attribute reduce algorithm is the key point of rough set research. However, it has been proved that finding the best reduction is the NP-hard problem. For the purpose of getting the reduction of systems effectively, an improved algorithm is put forward. The worst Fisher criterion was adopted as heuristic information to improve the searching efficiency and Max-Min Ant System was selected. Simplify the fault diagnosis decision table, then clear and concise decision rules can be obtained by rough sets theory. This method raises the accuracy and efficiency of fault diagnosis of bearing greatly.

Multivariate Decision Tree Algorithm Based on Rough Set

2012 ◽  
Vol 190-191 ◽  
pp. 347-351
Author(s):  
Bing Xiang Liu ◽  
Yan Wu ◽  
Meng Shan Li
Keyword(s):  
Decision Tree ◽  
Rough Set ◽  
Rough Sets ◽  
Inductive Learning ◽  
Classification Model ◽  
Classification Rules ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Rough Sets Theory ◽  
Sets Theory

The decision tree is a widely used classification model and inductive learning method based on examples. It is characterized by the simple classification rules, easy understanding for users and so on, but we also can see some disadvantages in certain situations. The paper puts forward the multivariable decision tree algorithm which based on a rough set to a combination of rough sets theory and decision tree algorithm. The multivariable decision tree algorithm has reduced the complexity of decision tree while not affect the readability of the classification rules. Experimental analysis has witnessed the feasibility and efficiency of the algorithm.

Application of Rough Sets Theory in Graduate Enrollment

2015 ◽  
Vol 713-715 ◽  
pp. 1640-1643 ◽  
Author(s):  
Juan Huang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Postgraduate Training ◽  
Rough Set Theory ◽  
Postgraduate Education ◽  
Rough Sets Theory ◽  
Graduate Enrollment ◽  
Enrollment Data ◽  
Sets Theory

Enrollment is the first step of the work of postgraduate education, and also a very crucial step. Therefore, in order to successfully carry out the postgraduate training work, be sure to do the enrollment work. In this paper, rough set theory is applied to the enrollment data of one college of a university. Then follow the general steps of data mining to research and analyze the enrollment data. Finally, draw some useful conclusions. These conclusions have a certain significance for graduate enrollment.

scholarly journals KNOWLEDGE REPRESENTING FEATURES IN ROUGH SETS THEORY

2015 ◽  
Vol 2 ◽  
pp. 169
Author(s):  
Oļegs Užga-Rebrovs
Keyword(s):  
Information Systems ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Mathematical Formalism ◽  
Rough Sets Theory ◽  
Sets Theory

Knowledge discovering and representing in information systems has evolved into an important area of research because of theoretical challenges and practical representing unknown knowledge in data. Rough set theory is a mathematical formalism for representing uncertainty that can be considered a special extension of the set theory. In this paper is analyzed knowledge representing problem and attribute signification’s problem, based on rough sets approach.

scholarly journals Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-33 ◽  
Author(s):  
Abbas Mardani ◽  
Mehrbakhsh Nilashi ◽  
Jurgita Antucheviciene ◽  
Madjid Tavana ◽  
Romualdas Bausys ◽  
...  
Keyword(s):  
Systematic Review ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Intelligent Systems ◽  
Rough Set Theory ◽  
Meta Analysis ◽  
Future Research ◽  
Fuzzy Rough Set ◽  
Rough Sets Theory

Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and meta-analysis approach, which is called “PRISMA,” has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

scholarly journals Rough Set Approach for Identifying the Combined Effects of Heat and Mass Transfer Due to MHD Nanofluid Flow over a Vertical ROTATING Frame

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1798
Author(s):  
Sumayyah I. Alshber ◽  
Hossam A. Nabwey
Keyword(s):  
Mass Transfer ◽  
Heat And Mass Transfer ◽  
Rough Set ◽  
Rough Sets ◽  
Combined Effects ◽  
Rotating Frame ◽  
Classification Rules ◽  
Nanofluid Flow ◽  
Rough Sets Theory ◽  
Sets Theory

The current work aims to investigate how to utilize rough set theory for generating a set of rules to investigate the combined effects of heat and mass transfer on entropy generation due to MHD nanofluid flow over a vertical rotating frame. The mathematical model describing the problem consists of nonlinear partial differential equations. By applying suitable transformations these equations are converted to non-dimensional form which are solved using a finite difference method known as “Runge-Kutta Fehlberg (RKF-45) method”. The obtained numerical results are depicted in tabular form and the basics of rough sets theory are applied to acquire all reductions. Finally; a set of generalized classification rules is extracted to predict the values of the local Nusselt number and the local Sherwood number. The resultant set of generalized classification rules demonstrate the novelty of the current work in using rough sets theory in the field of fluid dynamics effectively and can be considered as knowledge base with high accuracy and may be valuable in numerous engineering applications such as power production, thermal extrusion systems and microelectronics.

Sign in / Sign up

Export Citation Format

Share Document