scholarly journals Attribute reduction of relative knowledge granularity in intuitionistic fuzzy ordered decision table

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1727-1736 ◽  
Author(s):  
Binbin Sang ◽  
Xiaoyan Zhang ◽  
Weihua Xu
Keyword(s):  
Reduction Method ◽  
Attribute Reduction ◽  
Decision Table ◽  
Intuitionistic Fuzzy ◽  
Research Areas ◽  
Important Direction ◽  
The Moment ◽  
Complexity Of Algorithm ◽  
Knowledge Granularity ◽  
Conditional Attribute

For the moment, the attribute reduction algorithm of relative knowledge granularity is very important research areas. It provides a new viewpoint to simplify feature set. Based on the decision information is unchanged, fast and accurate deletion of redundant attributes, which is the meaning of attribute reduction. Distinguishing ability of attribute sets can be well described by relative knowledge granularity in domain. Therefore, how to use the information based on relative knowledge granularity to simplify the calculation of attribute reduction. It is an important direction of research. For increasing productiveness and accuracy of attribute reduction, in this paper we investigate attribute reduction method of relative knowledge granularity in intuitionistic fuzzy ordered decision table(IFODT). More precisely, we redefine the granularity of knowledge and the relative knowledge granularity by ordered relation. And their relevant properties are proved. On the premise that the decision results remain unchanged, in order to accurately calculate the relative importance of any condition attributes about the decision attribute sets, the conditional attribute of internal and external significance are designed by relative knowledge granularity. And some important properties of relative attribute significance are proved. Therefore, we determine the importance of conditional attributes based on the size of the relative attribute significance. In the aspect of computation, the corresponding algorithm is designed and time complexity of algorithm is calculated. Moreover, the attribute reduction model of relative knowledge granularity of efficiency and accuracy is proved by test. Last, the validity of algorithm is demonstrated by an case about IFODT.

scholarly journals Attribute Reduction Method Based on Sample Extraction and Priority

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Biqing Wang
Keyword(s):  
Rough Sets ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Equivalence Classes ◽  
Decision Table ◽  
Discernibility Matrix ◽  
Analysis Technique ◽  
Sample Extraction ◽  
Reduction Efficiency ◽  
Quick Sort

Abstract Attribute reduction is a key issue in the research of rough sets. Aiming at the shortcoming of attribute reduction algorithm based on discernibility matrix, an attribute reduction method based on sample extraction and priority is presented. Firstly, equivalence classes are divided using quick sort for computing compressed decision table. Secondly, important samples are extracted from compressed decision table using iterative self-organizing data analysis technique algorithm(ISODATA). Finally, attribute reduction of sample decision table is conducted based on the concept of priority. Experimental results show that the attribute reduction method based on sample extraction and priority can significantly reduce the overall execution time and improve the reduction efficiency.

Application of Rough Set Theory to Rule‐type Knowledge Discovery from Field Inspection Data on Highway Bridges

2017 ◽  
Author(s):  
Ayaho Miyamoto
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Highway Bridges ◽  
Decision Table ◽  
Field Inspection ◽  
Rule Type ◽  
Inspection Data ◽  
Contradictory Data

This paper describes an acquisitive method of rule‐type knowledge from the field inspection data on highway bridges. The proposed method is enhanced by introducing an improvement to a traditional data mining technique, i.e. applying the rough set theory to the traditional decision table reduction method. The new rough set theory approach helps in cases of exceptional and contradictory data, which in the traditional decision table reduction method are simply removed from analyses. Instead of automatically removing all apparently contradictory data cases, the proposed method determines whether the data really is contradictory and therefore must be removed or not. The method has been tested with real data on bridge members including girders and filled joints in bridges owned and managed by a highway corporation in Japan. There are, however, numerous inconsistent data in field data. A new method is therefore proposed to solve the problem of data loss. The new method reveals some generally unrecognized decision rules in addition to generally accepted knowledge. Finally, a computer program is developed to perform calculation routines, and some field inspection data on highway bridges is used to show the applicability of the proposed method.

New approaches to intuitionistic fuzzy-rough attribute reduction

2018 ◽  
Vol 34 (5) ◽  
pp. 3385-3394 ◽  
Author(s):  
Anoop Kumar Tiwari ◽  
Shivam Shreevastava ◽  
K.K. Shukla ◽  
Karthikeyan Subbiah
Keyword(s):  
Attribute Reduction ◽  
Intuitionistic Fuzzy ◽  
New Approaches

A high-dimensional attribute reduction method modeling and evaluation based on green economy data: evidence from 15 sub-provincial cities in China

2019 ◽  
Vol 24 (13) ◽  
pp. 9753-9764
Author(s):  
Gang Li ◽  
Jiaxiang Li ◽  
Yunqi Liu ◽  
Juan Liu ◽  
Baofeng Shi ◽  
...  
Keyword(s):  
Reduction Method ◽  
Attribute Reduction ◽  
Green Economy ◽  
High Dimensional

scholarly journals A Neighborhood Rough Sets-Based Attribute Reduction Method Using Lebesgue and Entropy Measures

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 138 ◽  
Author(s):  
Lin Sun ◽  
Lanying Wang ◽  
Jiucheng Xu ◽  
Shiguang Zhang
Keyword(s):  
Rough Sets ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Numerical Data ◽  
Classification Performance ◽  
Data Sets ◽  
Decision Systems ◽  
Public Data ◽  
Entropy Measures ◽  
Neighborhood Rough Sets

For continuous numerical data sets, neighborhood rough sets-based attribute reduction is an important step for improving classification performance. However, most of the traditional reduction algorithms can only handle finite sets, and yield low accuracy and high cardinality. In this paper, a novel attribute reduction method using Lebesgue and entropy measures in neighborhood rough sets is proposed, which has the ability of dealing with continuous numerical data whilst maintaining the original classification information. First, Fisher score method is employed to eliminate irrelevant attributes to significantly reduce computation complexity for high-dimensional data sets. Then, Lebesgue measure is introduced into neighborhood rough sets to investigate uncertainty measure. In order to analyze the uncertainty and noisy of neighborhood decision systems well, based on Lebesgue and entropy measures, some neighborhood entropy-based uncertainty measures are presented, and by combining algebra view with information view in neighborhood rough sets, a neighborhood roughness joint entropy is developed in neighborhood decision systems. Moreover, some of their properties are derived and the relationships are established, which help to understand the essence of knowledge and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is designed to improve the classification performance of large-scale complex data. The experimental results under an instance and several public data sets show that the proposed method is very effective for selecting the most relevant attributes with high classification accuracy.

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