scholarly journals A Parallel Attribute Reduction Method Based on Classification

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Deguang Li ◽  
Zhanyou Cui
Keyword(s):  
Rough Set Theory ◽  
Attribute Reduction ◽  
Development Trend ◽  
Divide And Conquer ◽  
Classification Method ◽  
Data Sets ◽  
Processing Efficiency ◽  
Knowledge Reduction ◽  
Computer Performance ◽  
Traditional Algorithm

Parallel processing as a method to improve computer performance has become a development trend. Based on rough set theory and divide-and-conquer idea of knowledge reduction, this paper proposes a classification method that supports parallel attribute reduction processing, the method makes the relative positive domain which needs to be calculated repeatedly independent, and the independent relative positive domain calculation could be processed in parallel; thus, attribute reduction could be handled in parallel based on this classification method. Finally, the proposed algorithm and the traditional algorithm are analyzed and compared by experiments, and the results show that the proposed method in this paper has more advantages in time efficiency, which proves that the method could improve the processing efficiency of attribute reduction and makes it more suitable for massive data sets.

scholarly journals Knowledge Reduction Based on Divide and Conquer Method in Rough Set Theory

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Feng Hu ◽  
Guoyin Wang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Recognition Rate ◽  
Large Data ◽  
Divide And Conquer ◽  
Data Sets ◽  
Levels Of Abstraction ◽  
Knowledge Reduction

The divide and conquer method is a typical granular computing method using multiple levels of abstraction and granulations. So far, although some achievements based on divided and conquer method in the rough set theory have been acquired, the systematic methods for knowledge reduction based on divide and conquer method are still absent. In this paper, the knowledge reduction approaches based on divide and conquer method, under equivalence relation and under tolerance relation, are presented, respectively. After that, a systematic approach, named as the abstract process for knowledge reduction based on divide and conquer method in rough set theory, is proposed. Based on the presented approach, two algorithms for knowledge reduction, including an algorithm for attribute reduction and an algorithm for attribute value reduction, are presented. Some experimental evaluations are done to test the methods on uci data sets and KDDCUP99 data sets. The experimental results illustrate that the proposed approaches are efficient to process large data sets with good recognition rate, compared with KNN, SVM, C4.5, Naive Bayes, and CART.

A Quick Algorithm for Attribute Reduction Based on Divide and Conquer Method

2011 ◽  
Vol 267 ◽  
pp. 92-97
Author(s):  
Feng Hu ◽  
Xi Chen ◽  
Xiao Yan Wang ◽  
Chuan Jiang Luo
Keyword(s):  
Heuristic Algorithm ◽  
High Efficiency ◽  
Simulation Experiment ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Divide And Conquer ◽  
Knowledge Reduction ◽  
High Efficient ◽  
Attribute Order ◽  
Small Parts

Knowledge reduction is one of the most important contributions of rough set theory. High efficient algorithm is necessary for attribute reduction. In this paper, combining given attribute order and quick sort method, a heuristic algorithm for attribute reduction is developed. In this method, the objects in universe are divided into many small parts on the attributes by divide and conquer method, and different granules in different granular levels are obtained. The small ones can be processed quickly, so a fast heuristic algorithm for attribute reduction is proposed. Simulation experiment results illustrate the high efficiency of the improved knowledge reduction algorithm.

scholarly journals Heuristic Approaches to Attribute Reduction for Generalized Decision Preservation

2019 ◽  
Vol 9 (14) ◽  
pp. 2841 ◽  
Author(s):  
Nan Zhang ◽  
Xueyi Gao ◽  
Tianyou Yu
Keyword(s):  
Large Scale ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
High Dimensional ◽  
Data Sets ◽  
Challenging Problem ◽  
Research Fields ◽  
Heuristic Strategies ◽  
Significance Measures ◽  
Original Information

Attribute reduction is a challenging problem in rough set theory, which has been applied in many research fields, including knowledge representation, machine learning, and artificial intelligence. The main objective of attribute reduction is to obtain a minimal attribute subset that can retain the same classification or discernibility properties as the original information system. Recently, many attribute reduction algorithms, such as positive region preservation, generalized decision preservation, and distribution preservation, have been proposed. The existing attribute reduction algorithms for generalized decision preservation are mainly based on the discernibility matrix and are, thus, computationally very expensive and hard to use in large-scale and high-dimensional data sets. To overcome this problem, we introduce the similarity degree for generalized decision preservation. On this basis, the inner and outer significance measures are proposed. By using heuristic strategies, we develop two quick reduction algorithms for generalized decision preservation. Finally, theoretical and experimental results show that the proposed heuristic reduction algorithms are effective and efficient.

The Incremental Knowledge Acquisition Based on Hash Algorithm

2016 ◽  
Vol 24 (03) ◽  
pp. 347-366 ◽  
Author(s):  
Qing-Hua Zhang ◽  
Long-Yang Yao ◽  
Guan-Sheng Zhang ◽  
Yu-Ke Xin
Keyword(s):  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Algorithm Analysis ◽  
Data Sets ◽  
Hash Algorithm ◽  
Acquisition Method

In this paper, a new incremental knowledge acquisition method is proposed based on rough set theory, decision tree and granular computing. In order to effectively process dynamic data, describing the data by rough set theory, computing equivalence classes and calculating positive region with hash algorithm are analyzed respectively at first. Then, attribute reduction, value reduction and the extraction of rule set by hash algorithm are completed efficiently. Finally, for each new additional data, the incremental knowledge acquisition method is proposed and used to update the original rules. Both algorithm analysis and experiments show that for processing the dynamic information systems, compared with the traditional algorithms and the incremental knowledge acquisition algorithms based on granular computing, the time complexity of the proposed algorithm is lower due to the efficiency of hash algorithm and also this algorithm is more effective when it is used to deal with the huge data sets.

Research of Improved Attribute Reduction Algorithm Based on Data Mining of Rough Set

2014 ◽  
Vol 644-650 ◽  
pp. 2120-2123 ◽  
Author(s):  
De Zhi An ◽  
Guang Li Wu ◽  
Jun Lu
Keyword(s):  
Data Mining ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Reduction Algorithm ◽  
The Core ◽  
Rules Extraction

At present there are many data mining methods. This paper studies the application of rough set method in data mining, mainly on the application of attribute reduction algorithm based on rough set in the data mining rules extraction stage. Rough set in data mining is often used for reduction of knowledge, and thus for the rule extraction. Attribute reduction is one of the core research contents of rough set theory. In this paper, the traditional attribute reduction algorithm based on rough sets is studied and improved, and for large data sets of data mining, a new attribute reduction algorithm is proposed.

scholarly journals About a Fuzzy Distance between Two Fuzzy Partitions and Application in Attribute Reduction Problem

2016 ◽  
Vol 16 (4) ◽  
pp. 13-28 ◽  
Author(s):  
Cao Chinh Nghia ◽  
Demetrovics Janos ◽  
Nguyen Long Giang ◽  
Vu Duc Thi
Keyword(s):  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Data Sets ◽  
Theory Approach ◽  
Fuzzy Rough Set ◽  
Fuzzy Distance ◽  
Reduction Methods ◽  
Fuzzy Partitions ◽  
Decision Tables

Abstract According to traditional rough set theory approach, attribute reduction methods are performed on the decision tables with the discretized value domain, which are decision tables obtained by discretized data methods. In recent years, researches have proposed methods based on fuzzy rough set approach to solve the problem of attribute reduction in decision tables with numerical value domain. In this paper, we proposeafuzzy distance between two partitions and an attribute reduction method in numerical decision tables based on proposed fuzzy distance. Experiments on data sets show that the classification accuracy of proposed method is more efficient than the ones based fuzzy entropy.

scholarly journals Metric Based Attribute Reduction Method in Dynamic Decision Tables

2016 ◽  
Vol 16 (2) ◽  
pp. 3-15 ◽  
Author(s):  
Demetrovics Janos ◽  
Nguyen Thi Lan Huong ◽  
Vu Duc Thi ◽  
Nguyen Long Giang
Keyword(s):  
Feature Selection ◽  
Rough Set Theory ◽  
Distance Measure ◽  
Attribute Reduction ◽  
Knowledge Discovery In Databases ◽  
Data Sets ◽  
Decision Systems ◽  
Incremental Methods ◽  
Static Data ◽  
Dynamic Databases

Abstract Feature selection is a vital problem which needs to be effectively solved in knowledge discovery in databases and pattern recognition due to two basic reasons: minimizing costs and accurately classifying data. Feature selection using rough set theory is also called attribute reduction. It has attracted a lot of attention from researchers and numerous potential results have been gained. However, most of them are applied on static data and attribute reduction in dynamic databases is still in its early stages. This paper focuses on developing incremental methods and algorithms to derive reducts, employing a distance measure when decision systems vary in condition attribute set. We also conduct experiments on UCI data sets and the experimental results show that the proposed algorithms are better in terms of time consumption and reducts’ cardinality in comparison with non-incremental heuristic algorithm and the incremental approach using information entropy proposed by authors in [17].

scholarly journals An Attribute Reduction Method using Neighborhood Entropy Measures in Neighborhood Rough Sets

Entropy ◽  
2019 ◽  
Vol 21 (2) ◽  
pp. 155 ◽  
Author(s):  
Lin Sun ◽  
Xiaoyu Zhang ◽  
Jiucheng Xu ◽  
Shiguang Zhang
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Classification Performance ◽  
Data Sets ◽  
Complex Data ◽  
Decision Systems ◽  
Neighborhood Rough Sets

Attribute reduction as an important preprocessing step for data mining, and has become a hot research topic in rough set theory. Neighborhood rough set theory can overcome the shortcoming that classical rough set theory may lose some useful information in the process of discretization for continuous-valued data sets. In this paper, to improve the classification performance of complex data, a novel attribute reduction method using neighborhood entropy measures, combining algebra view with information view, in neighborhood rough sets is proposed, which has the ability of dealing with continuous data whilst maintaining the classification information of original attributes. First, to efficiently analyze the uncertainty of knowledge in neighborhood rough sets, by combining neighborhood approximate precision with neighborhood entropy, a new average neighborhood entropy, based on the strong complementarity between the algebra definition of attribute significance and the definition of information view, is presented. Then, a concept of decision neighborhood entropy is investigated for handling the uncertainty and noisiness of neighborhood decision systems, which integrates the credibility degree with the coverage degree of neighborhood decision systems to fully reflect the decision ability of attributes. Moreover, some of their properties are derived and the relationships among these measures are established, which helps to understand the essence of knowledge content and the uncertainty of neighborhood decision systems. Finally, a heuristic attribute reduction algorithm is proposed to improve the classification performance of complex data sets. The experimental results under an instance and several public data sets demonstrate that the proposed method is very effective for selecting the most relevant attributes with great classification performance.

scholarly journals A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hua Li ◽  
Deyu Li ◽  
Yanhui Zhai ◽  
Suge Wang ◽  
Jing Zhang
Keyword(s):  
Feature Selection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Mathematical Tool ◽  
Knowledge Reduction ◽  
Variable Precision ◽  
Multilabel Learning ◽  
Decision Tables

Owing to the high dimensionality of multilabel data, feature selection in multilabel learning will be necessary in order to reduce the redundant features and improve the performance of multilabel classification. Rough set theory, as a valid mathematical tool for data analysis, has been widely applied to feature selection (also called attribute reduction). In this study, we propose a variable precision attribute reduct for multilabel data based on rough set theory, calledδ-confidence reduct, which can correctly capture the uncertainty implied among labels. Furthermore, judgement theory and discernibility matrix associated withδ-confidence reduct are also introduced, from which we can obtain the approach to knowledge reduction in multilabel decision tables.

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