A Rough Set-Based Heuristic Algorithm for Attribute Reduction

2008 ◽  
Author(s):  
Zhang Yingjun ◽  
Zhu Feixiang ◽  
Xing Shengwei
Keyword(s):  
Rough Set ◽  
Heuristic Algorithm ◽  
Attribute Reduction

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.

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.

An Algorithm of Attribute Reduct Based on Rough Set

2013 ◽  
Vol 347-350 ◽  
pp. 3177-3181 ◽  
Author(s):  
Gui Juan Song ◽  
Gang Li
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Heuristic Algorithm ◽  
Basic Concept ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Conditional Entropy ◽  
Actual Application ◽  
Np Problem

Attribute reduction is a key problem for rough set theory. While computing reduction according to the definitions is a typical NP problem. In this paper, basic concept of rough set theory is presented, one heuristic algorithm for attribution reduction based on conditional entropy is proposed. The actual application shows that the method is feasible and effective

scholarly journals Parametric Matroid of Rough Set

2015 ◽  
Vol 23 (06) ◽  
pp. 893-908 ◽  
Author(s):  
Yanfang Liu ◽  
Hong Zhao ◽  
William Zhu
Keyword(s):  
Rough Set ◽  
Equivalence Relation ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Independent Set ◽  
Approximation Operator ◽  
Approximation Number ◽  
Lower Approximation ◽  
The One

Rough set is mainly concerned with the approximations of objects through an equivalence relation on a universe. Matroid is a generalization of linear algebra and graph theory. Recently, a matroidal structure of rough sets is established and applied to the problem of attribute reduction which is an important application of rough set theory. In this paper, we propose a new matroidal structure of rough sets and call it a parametric matroid. On the one hand, for an equivalence relation on a universe, a parametric set family, with any subset of the universe as its parameter, is defined through the lower approximation operator. This parametric set family is proved to satisfy the independent set axiom of matroids, therefore a matroid is generated, and we call it a parametric matroid of the rough set. Through the lower approximation operator, three equivalent representations of the parametric set family are obtained. Moreover, the parametric matroid of the rough set is proved to be the direct sum of a partition-circuit matroid and a free matroid. On the other hand, partition-circuit matroids are well studied through the lower approximation number, and then we use it to investigate the parametric matroid of the rough set. Several characteristics of the parametric matroid of the rough set, such as independent sets, bases, circuits, the rank function and the closure operator, are expressed by the lower approximation number.

Research on RS-WNN Integrated Modeling

2011 ◽  
Vol 105-107 ◽  
pp. 2169-2173
Author(s):  
Zong Chang Xu ◽  
Xue Qin Tang ◽  
Shu Feng Huang
Keyword(s):  
Neural Network ◽  
Rough Set ◽  
Attribute Reduction ◽  
Wavelet Neural Network ◽  
Integrated Modeling ◽  
Generalization Ability ◽  
Decision Attributes ◽  
Optimum Decision ◽  
Structure Complexity ◽  
Modeling Algorithm

Wavelet Neural Network (WNN) integration modeling based on Rough Set (RS) is studied. An integration modeling algorithm named RS-WNN, which first introduces a heuristic attribute reduction recursion algorithm to determine the optimum decision attributes and then conducts WNN modeling, is proposed. This method is adopted to more effectively eliminate the redundant attributes, lower the structure complexity of WNN, which reduce the time of training and improve the generalization ability of WNN. The result of the experiment shows this method is superior and efficient.

Sign in / Sign up

Export Citation Format

Share Document