scholarly journals Rough Set Approach to Approximation Reduction in Ordered Decision Table with Fuzzy Decision

2011 ◽  
Vol 2011 ◽  
pp. 1-16 ◽  
Author(s):  
Xiaoyan Zhang ◽  
Shihu Liu ◽  
Weihua Xu
Keyword(s):  
Information Systems ◽  
Knowledge Discovery ◽  
Rough Set ◽  
Decision Table ◽  
Fuzzy Decision ◽  
Efficient Tool ◽  
Lower Approximation ◽  
Dominance Relations ◽  
Decision Tables

In practice, some of information systems are based on dominance relations, and values of decision attribute are fuzzy. So, it is meaningful to study attribute reductions in ordered decision tables with fuzzy decision. In this paper, upper and lower approximation reductions are proposed in this kind of complicated decision table, respectively. Some important properties are discussed. The judgement theorems and discernibility matrices associated with two reductions are obtained from which the theory of attribute reductions is provided in ordered decision tables with fuzzy decision. Moreover, rough set approach to upper and lower approximation reductions is presented in ordered decision tables with fuzzy decision as well. An example illustrates the validity of the approach, and results show that it is an efficient tool for knowledge discovery in ordered decision tables with fuzzy decision.

scholarly journals Lower Approximation Reduction Based on Discernibility Information Tree in Inconsistent Ordered Decision Information Systems

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 696 ◽  
Author(s):  
Jia Zhang ◽  
Xiaoyan Zhang ◽  
Weihua Xu
Keyword(s):  
Information System ◽  
Information Systems ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Common Method ◽  
New Method ◽  
Lower Approximation ◽  
Dominance Relations

Attribute reduction is an important topic in the research of rough set theory, and it has been widely used in many aspects. Reduction based on an identifiable matrix is a common method, but a lot of space is occupied by repetitive and redundant identifiable attribute sets. Therefore, a new method for attribute reduction is proposed, which compresses and stores the identifiable attribute set by a discernibility information tree. In this paper, the discernibility information tree based on a lower approximation identifiable matrix is constructed in an inconsistent decision information system under dominance relations. Then, combining the lower approximation function with the discernibility information tree, a complete algorithm of lower approximation reduction based on the discernibility information tree is established. Finally, the rationality and correctness of this method are verified by an example.

Fuzzy Attribute Reduction Based on Fuzzy Similarity

2014 ◽  
Vol 533 ◽  
pp. 237-241
Author(s):  
Xiao Jing Liu ◽  
Wei Feng Du ◽  
Xiao Min
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Equivalence Relation ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Decision Table ◽  
Reduction Algorithm ◽  
Fuzzy Decision ◽  
Core Content ◽  
Fuzzy Similarity

The measure of the significance of the attribute and attribute reduction is one of the core content of rough set theory. The classical rough set model based on equivalence relation, suitable for dealing with discrete-valued attributes. Fuzzy-rough set theory, integrating fuzzy set and rough set theory together, extending equivalence relation to fuzzy relation, can deal with fuzzy-valued attributes. By analyzing three problems of FRAR which is a fuzzy decision table attribute reduction algorithm having extensive use, this paper proposes a new reduction algorithm which has better overcome the problem, can handle larger fuzzy decision table. Experimental results show that our reduction algorithm is much quicker than the FRAR algorithm.

Deep learning architecture using rough sets and rough neural networks

Kybernetes ◽  
2017 ◽  
Vol 46 (4) ◽  
pp. 693-705 ◽  
Author(s):  
Yasser F. Hassan
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Soft Computing ◽  
Rough Set ◽  
Real World ◽  
Rough Sets ◽  
Decision Table ◽  
Content Type ◽  
Real World Applications ◽  
Decision Tables

Purpose This paper aims to utilize machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications. Design/methodology/approach The objective of this work is to propose a model for deep rough set theory that uses more than decision table and approximating these tables to a classification system, i.e. the paper propose a novel framework of deep learning based on multi-decision tables. Findings The paper tries to coordinate the local properties of individual decision table to provide an appropriate global decision from the system. Research limitations/implications The rough set learning assumes the existence of a single decision table, whereas real-world decision problem implies several decisions with several different decision tables. The new proposed model can handle multi-decision tables. Practical implications The proposed classification model is implemented on social networks with preferred features which are freely distribute as social entities with accuracy around 91 per cent. Social implications The deep learning using rough sets theory simulate the way of brain thinking and can solve the problem of existence of different information about same problem in different decision systems Originality/value This paper utilizes machine learning and soft computing to propose a new method of rough sets using deep learning architecture for many real-world applications.

scholarly journals A Method for Extracting Knowledge from Decision Tables in Terms of Functional Dependencies

2013 ◽  
Vol 13 (1) ◽  
pp. 73-82 ◽  
Author(s):  
Vu Duc Thi ◽  
Nguyen Long Giang
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Relational Database ◽  
Time Complexity ◽  
Decision Table ◽  
Functional Dependencies ◽  
Worst Case ◽  
Decision Tables

Abstract The problem of extracting knowledge from decision tables in terms of functional dependencies is one of the important problems in knowledge discovery and data mining. Based on some results in relational database, in this paper we propose two algorithms. The first one is to find all reducts of a consistent decision table. The second is to infer functional dependencies from a consistent decision table. The second algorithm is based on the result of the first. We show that the time complexity of the two algorithms proposed is exponential in the number of attributes in the worst case.

Attribute Reduction Algorithm for Fuzzy Decision Table with Membership Degrees and Weights

2012 ◽  
Vol 198-199 ◽  
pp. 1367-1371
Author(s):  
Hao Dong Zhu ◽  
Hong Chan Li
Keyword(s):  
Rough Set ◽  
Decision Procedure ◽  
Attribute Reduction ◽  
Decision Table ◽  
Membership Degree ◽  
Reduction Algorithm ◽  
Fuzzy Decision ◽  
Definite Form

The classical rough set can not show the fuzziness and the importance of objects in decision procedure because it uses definite form to express each object. In order to solve this problem, this paper firstly introduces a special decision table in which each object has a membership degree to show its fuzziness and has been assigned a weight to show its importance in decision procedure. And then, the special decision table is studied and the relevant rough set model is provided. In the meantime, relevant definitions and theorems are proposed. On the above basis, an attribute reduction algorithm is presented. Finally, feasibility of the relevant rough set model and the presented attribute reduction algorithm are verified by an example.

On Variable Precision Generalized Rough Sets and Incomplete Decision Tables

2021 ◽  
Vol 179 (1) ◽  
pp. 75-92
Author(s):  
Yu-Ru Syau ◽  
Churn-Jung Liau ◽  
En-Bing Lin
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Decision Table ◽  
Necessary Condition ◽  
Weak Consistency ◽  
Decision System ◽  
Variable Precision ◽  
Generalized Rough Set ◽  
Decision Tables ◽  
Generalized Rough Sets

We present variable precision generalized rough set approach to characterize incomplete decision tables. We show how to determine the discernibility threshold for a reflexive relational decision system in the variable precision generalized rough set model. We also point out some properties of positive regions and prove a statement of the necessary condition for weak consistency of an incomplete decision table. We present two examples to illustrate the results obtained in this paper.

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