A Heuristic Algorithm for Attribute Reduction of Decision-making Problem Based on Rough Set

2006 ◽  
Author(s):  
Chang-rui Yu ◽  
Hong-wei Wang ◽  
Yan Luo
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Heuristic Algorithm ◽  
Attribute Reduction ◽  
Decision Making Problem

scholarly journals Multi-Attribute Decision Making Based on Attribute Importance Degree and Case-Based Reasoning

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 62-74 ◽  
Author(s):  
Zhong Wu ◽  
Ruixia Yan
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Rough Set Theory ◽  
Importance Measure ◽  
Case Based Reasoning ◽  
Relative Importance ◽  
Attribute Importance ◽  
Decision Making Problem ◽  
Multi Attribute Decision Making ◽  
Case Based

Abstract To tackle a multi-attribute decision making problem, rough set and casebased reasoning are often combined. However, the reduction in a rough set is always complex. In this paper we provide a new relative importance measure about the unitary attributes values by ranking the relative importance of the attributes in the rough set theory. A new rough set model based on ranking the relative importance of the attributes is built and its properties are studied. Then unitary attributes values are utilized to compute the similarity of rules in case-based reasoning, for there might be incompletely match or miss values. A new multiattribute decision making based on case-based reasoning and a rough set based on the ranking relative importance of the attributes is constructed, which obtains rules, avoiding reduction and rule extraction.

scholarly journals Neighborhood Attribute Reduction: A Multicriterion Strategy Based on Sample Selection

Information ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 282 ◽  
Author(s):  
Yuan Gao ◽  
Xiangjian Chen ◽  
Xibei Yang ◽  
Pingxin Wang
Keyword(s):  
Rough Set ◽  
Heuristic Algorithm ◽  
Sample Selection ◽  
Attribute Reduction ◽  
Classification Performance ◽  
Multiple Criteria ◽  
Experimental Results ◽  
Time Consumption ◽  
Redundant Data ◽  
Selection For

In the rough-set field, the objective of attribute reduction is to regulate the variations of measures by reducing redundant data attributes. However, most of the previous concepts of attribute reductions were designed by one and only one measure, which indicates that the obtained reduct may fail to meet the constraints given by other measures. In addition, the widely used heuristic algorithm for computing a reduct requires to scan all samples in data, and then time consumption may be too high to be accepted if the size of the data is too large. To alleviate these problems, a framework of attribute reduction based on multiple criteria with sample selection is proposed in this paper. Firstly, cluster centroids are derived from data, and then samples that are far away from the cluster centroids can be selected. This step completes the process of sample selection for reducing data size. Secondly, multiple criteria-based attribute reduction was designed, and the heuristic algorithm was used over the selected samples for computing reduct in terms of multiple criteria. Finally, the experimental results over 12 UCI datasets show that the reducts obtained by our framework not only satisfy the constraints given by multiple criteria, but also provide better classification performance and less time consumption.

scholarly journals Pseudolabel Decision-Theoretic Rough Set

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Xun Wang ◽  
Wendong Zhang ◽  
Dun Liu ◽  
Hualong Yu ◽  
Xibei Yang ◽  
...  
Keyword(s):  
Data Analysis ◽  
Rough Set ◽  
Heuristic Algorithm ◽  
Rough Sets ◽  
Attribute Reduction ◽  
Experimental Results ◽  
Data Sets ◽  
Binary Relations ◽  
Sensitivity Problem ◽  
Neighborhood Relation

In decision-theoretic rough set (DTRS), the decision costs are used to generate the thresholds for characterizing the probabilistic approximations. Similar to other rough sets, many generalized DTRS can also be formed by using different binary relations. Nevertheless, it should be noticed that most of the processes for calculating binary relations do not take the labels of samples into account, which may lead to the lower discrimination; for example, samples with different labels are regarded as indistinguishable. To fill such gap, the main contribution of this paper is to propose a pseudolabel strategy for constructing new DTRS. Firstly, a pseudolabel neighborhood relation is presented, which can differentiate samples by not only the neighborhood technique but also the pseudolabels of samples. Immediately, the pseudolabel neighborhood decision-theoretic rough set (PLNDTRS) can be constructed. Secondly, the problem of attribute reduction is explored, which aims to further reduce the PLNDTRS related decision costs. A heuristic algorithm is also designed to find such reduct. Finally, the clustering technique is employed to generate the pseudolabels of samples; the experimental results over 15 UCI data sets tell us that PLNDTRS is superior to DTRS without using pseudolabels because the former can generate lower decision costs. Moreover, the proposed heuristic algorithm is also effective in providing satisfied reducts. This study suggests new trends concerning cost sensitivity problem in rough data analysis.

Research on Rough Set Theory-Based Customer Division

2014 ◽  
Vol 989-994 ◽  
pp. 1536-1540
Author(s):  
Gui Juan Song ◽  
Xin Cao ◽  
Xin Yue Wang
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Decision Rules ◽  
Main Idea ◽  
Rule Extraction

The main idea of rough set theory is to extract decision rules by attribute reduction and value reduction in the premises of keeping the ability of classification. This paper presents the design of model for customer division based on rough set, and uses algorithms for attribute reduction and rule extraction in rough set to analyze the customer of supermarket. This paper also introduces how to achieve the minimum result of attribute reduction and decision-making via decision-making report, winkling redundant attribute and over-rule of decision.

scholarly journals A Rough Multi-Criteria Decision-Making Approach for Sustainable Supplier Selection under Vague Environment

2018 ◽  
Vol 10 (8) ◽  
pp. 2622 ◽  
Author(s):  
Huiyun Lu ◽  
Shaojun Jiang ◽  
Wenyan Song ◽  
Xinguo Ming
Keyword(s):  
Decision Making ◽  
Set Theory ◽  
Rough Set ◽  
Supplier Selection ◽  
Rough Set Theory ◽  
Multi Criteria Decision Making ◽  
Sustainable Supply Chain ◽  
Sustainable Supplier Selection ◽  
Decision Making Problem ◽  
Priori Information

With the growing awareness of environmental and social issues, sustainable supply chain management (SSCM) has received considerable attention both in academia and industry. Supplier selection plays an important role in the successful implementation of sustainable supply chain management, because it can influence the performance of SSCM. Sustainable supplier selection is a typical multi-criteria decision-making problem involving subjectivity and vagueness. Although some previous researches of supplier selection use fuzzy approaches to deal with vague information, it has been criticized for requiring much priori information and inflexibility in manipulating vagueness. Moreover, the previous methods often omit the environmental and social evaluation criteria in the supplier selection. To manipulate these problems, a new approach based on the rough set theory and ELECTRE (ELimination Et Choix Traduisant la REalité) is developed in this paper. The novel approach integrates the strength of rough set theory in handling vagueness without much priori information and the merit of ELECTRE in modeling multi-criteria decision-making problem. Finally, a case study of sustainable supplier selection for solar air-conditioner manufacturer is provided to demonstrate the application and potential of the approach.

scholarly journals Adjustable Fuzzy Rough Reduction: A Nested Strategy

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ying Shi ◽  
Hui Qi ◽  
Xiaofang Mu ◽  
Mingxing Hou
Keyword(s):  
Rough Set ◽  
Heuristic Algorithm ◽  
Computational Efficiency ◽  
Attribute Reduction ◽  
Fuzzy Rough Set ◽  
Attribute Evaluation ◽  
Traditional Approaches

As a crucial extension of Pawlak's rough set, a fuzzy rough set has been successfully applied in real-valued attribute reduction. Nevertheless, the traditional fuzzy rough set is not provided with adjustable ability due to the maximal and minimal operators. It follows that the associated measure for attribute evaluation is not always appropriate. To alleviate such problems, a novel adjustable fuzzy rough set model is presented and further introduced into the parameterized attribute reduction. Additionally, the inner relationship between the appointed parameter and the reduct result is discovered, and thereby a nested mechanism is adopted to accelerate the searching procedure of reduct. Experiments demonstrate that the proposed heuristic algorithm can offer us more stable reducts with higher computational efficiency as compared with the traditional approaches.

scholarly journals Stable attribute reduction for neighborhood rough set

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1809-1815
Author(s):  
Shaochen Liang ◽  
Xibei Yang ◽  
Xiangjian Chen ◽  
Jingzheng Li
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Heuristic Algorithm ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Reduction Process ◽  
Neighborhood Rough Set ◽  
The Stability ◽  
Voting Strategy ◽  
The Given

In neighborhood rough set theory, traditional heuristic algorithm for computing reducts does not take the stability of the selected attributes into account, it follows that the performances of the reducts may not be good enough if the perturbations of data occur. To fill the gap, the mechanism of acquiring the most significant attribute is realized by two steps in the reduction process: firstly, several important attributes are derived in each iteration based on several radii which are close to the given radius for computing reduct; secondly, the most significant attribute is selected from them by a voting strategy. The experiments verify that such method can effectively improve the stabilities of the reducts, and it does not require too much attributes for constructing the reducts.

Sign in / Sign up

Export Citation Format

Share Document