Taxonomy of rough set approaches for rule generation

2021 ◽  
Author(s):  
Murni Marbun ◽  
Opim Salim Sitompul ◽  
Erna Budhiarti Nababan ◽  
Poltak Sihombing
Keyword(s):  
Rough Set ◽  
Rule Generation

Image Segmentation Using Rough Set Theory

2014 ◽  
Vol 1 (2) ◽  
pp. 62-74 ◽  
Author(s):  
Payel Roy ◽  
Srijan Goswami ◽  
Sayan Chakraborty ◽  
Ahmad Taher Azar ◽  
Nilanjan Dey
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Gray Level ◽  
Rule Generation ◽  
Local Minima ◽  
Fuzzy Correlation ◽  
Segmentation Methods

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

An Evaluation Method of Relative Reducts Based on Roughness of Partitions

2010 ◽  
Vol 4 (2) ◽  
pp. 50-62 ◽  
Author(s):  
Yasuo Kudo ◽  
Tetsuya Murai
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Evaluation Method ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Evaluation Criterion ◽  
Rule Generation ◽  
Mathematical Foundations

This paper focuses on rough set theory which provides mathematical foundations of set-theoretical approximation for concepts, as well as reasoning about data. Also presented in this paper is the concept of relative reducts which is one of the most important notions for rule generation based on rough set theory. In this paper, from the viewpoint of approximation, the authors introduce an evaluation criterion for relative reducts using roughness of partitions that are constructed from relative reducts. The proposed criterion evaluates each relative reduct by the average of coverage of decision rules based on the relative reduct, which also corresponds to evaluate the roughness of partition constructed from the relative reduct,

scholarly journals Rule Generation Based on Novel Kernel Intuitionistic Fuzzy Rough Set Model

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 11953-11958 ◽  
Author(s):  
Kuo-Ping Lin ◽  
Kuo-Chen Hung ◽  
Ching-Lin Lin
Keyword(s):  
Rough Set ◽  
Rule Generation ◽  
Fuzzy Rough Set ◽  
Intuitionistic Fuzzy

Rough Sets Based Rule Generation from Data with Categorical and Numerical Values

2008 ◽  
Vol 12 (5) ◽  
pp. 426-434 ◽  
Author(s):  
Hiroshi Sakai ◽  
◽  
Kazuhiro Koba ◽  
Michinori Nakata ◽  
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Rule Generation ◽  
Utility Program ◽  
New Framework

Rough set theory has been mainly applied to data with categorical values. In order to handle data with numerical values in this theory, a familiar concept of ‘wildcards’ was employed, and a new framework of rough sets based rule generation has been proposed. Two characters @ and # were introduced into this framework, and numerical patterns were also defined for numerical values. The concepts of ‘coarse’ and ‘fine’ for rules were explicitly defined according to numerical patterns. This paper enhances the previous framework, and describes the implementation of an utility program. This utility program is applied to the data in UCI Machine Learning Repository, and some useful rules are obtained.

Image Segmentation Using Rough Set Theory

2017 ◽  
pp. 1414-1426 ◽  
Author(s):  
Payel Roy ◽  
Srijan Goswami ◽  
Sayan Chakraborty ◽  
Ahmad Taher Azar ◽  
Nilanjan Dey
Keyword(s):  
Image Processing ◽  
Image Segmentation ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Gray Level ◽  
Rule Generation ◽  
Local Minima ◽  
Fuzzy Correlation ◽  
Segmentation Methods

In the domain of image processing, image segmentation has become one of the key application that is involved in most of the image based operations. Image segmentation refers to the process of breaking or partitioning any image. Although, like several image processing operations, image segmentation also faces some problems and issues when segmenting process becomes much more complicated. Previously lot of work has proved that Rough-set theory can be a useful method to overcome such complications during image segmentation. The Rough-set theory helps in very fast convergence and in avoiding local minima problem, thereby enhancing the performance of the EM, better result can be achieved. During rough-set-theoretic rule generation, each band is individualized by using the fuzzy-correlation-based gray-level thresholding. Therefore, use of Rough-set in image segmentation can be very useful. In this paper, a summary of all previous Rough-set based image segmentation methods are described in detail and also categorized accordingly. Rough-set based image segmentation provides a stable and better framework for image segmentation.

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