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 Fuzzy Distance Based Attribute Reduction in Decision Tables

2017 ◽  
pp. 104
Author(s):  
Cao Chinh Nghia ◽  
Vu Duc Thi ◽  
Nguyen Long Giang ◽  
Tan Hanh
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Data Sets ◽  
Fuzzy Rough Set ◽  
Fuzzy Distance ◽  
Reduction Methods ◽  
Decision Tables ◽  
Attribute Value

In recent years, fuzzy rough set based attribute reduction has attracted the interest of many researchers. The attribute reduction methods can perform directly on the decision tables with numerical attribute value domain. In this paper, we propose a fuzzy distance based attribute reduction method on the decision table with numerical attribute value domain. Experiments on data sets show that the proposed method is more efficient than the ones based on Shannon’s entropy on the executed time and the classification accuracy of reduct.

scholarly journals About a Distance Measure and Application for Finding Reduct in Incomplete Decision Tables

2019 ◽  
Vol 9 (1) ◽  
pp. 6294-6298
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Distance Measure ◽  
Attribute Reduction ◽  
Data Sets ◽  
Reduction Methods ◽  
Tolerance Rough Set ◽  
Decision Tables ◽  
Filter Algorithms ◽  
Over 40 Years

Tolerance rough set model is an effective tool to reduce attributes in incomplete decision tables. Over 40 years, several attribute reduction methods have been proposed to improve the efficiency of execution time and the number of attributes of the reduct. However, they are classical filter algorithms, in which the classification accuracy of decision tables is computed after obtaining the reducts. Therefore, the obtained reducts of these algorithms are not optimal in terms of reduct cardinality and classification accuracy. In this paper, we propose a filter-wrapper algorithm to find a reduct in incomplete decision tables. We then use this measure to determine the importance of the property and select the attribute based on the calculated importance (filter phase). In the next step, we find the reduct with the highest classification accuracy by iterating over elements of the set containing the sequence of attributes selected in the first step (wrapper phase). To verify the effectiveness of the method, we conduct experiments on 6 famous UCI data sets. Experimental results show that the proposed method increase classification accuracy as well as reduce the cardinality of reduct compared to Algorithm 1 [12].

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 An Efficient Classification Model using Fuzzy Rough Set Theory and Random Weight Neural Network

2021 ◽  
Vol 5 (3) ◽  
pp. 92-108
Author(s):  
Rana Aamir Raza
Keyword(s):  
Neural Network ◽  
Predictive Model ◽  
Set Theory ◽  
Rough Set ◽  
Large Scale ◽  
Rough Set Theory ◽  
Classification Model ◽  
Data Sets ◽  
Fuzzy Rough Set ◽  
Random Weight

In the area of fuzzy rough set theory (FRST), researchers have gained much interest in handling the high-dimensional data. Rough set theory (RST) is one of the important tools used to pre-process the data and helps to obtain a better predictive model, but in RST, the process of discretization may loss useful information. Therefore, fuzzy rough set theory contributes well with the real-valued data. In this paper, an efficient technique is presented based on Fuzzy rough set theory (FRST) to pre-process the large-scale data sets to increase the efficacy of the predictive model. Therefore, a fuzzy rough set-based feature selection (FRSFS) technique is associated with a Random weight neural network (RWNN) classifier to obtain the better generalization ability. Results on different dataset show that the proposed technique performs well and provides better speed and accuracy when compared by associating FRSFS with other machine learning classifiers (i.e., KNN, Naive Bayes, SVM, decision tree and backpropagation neural network).

A Parallel Computation Method for Heuristic Attribute Reduction Using Reduced Decision Tables

2013 ◽  
Vol 17 (3) ◽  
pp. 371-376 ◽  
Author(s):  
Yasuo Kudo ◽  
◽  
Tetsuya Murai ◽  
Keyword(s):  
Data Mining ◽  
Parallel Computation ◽  
Set Theory ◽  
Rough Set ◽  
Reduction Method ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Computation Method ◽  
Decision Tables

In this paper, we propose a parallel computation framework for a heuristic attribute reduction method. Attribute reduction is a key technique to use rough set theory as a tool in data mining. The authors have previously proposed a heuristic attribute reduction method to compute as many relative reducts as possible from a given dataset with numerous attributes. We parallelize our method by using open multiprocessing. We also evaluate the performance of a parallelized attribute reduction method by experiments.

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.

scholarly journals Neighborhood Based Multi-Granularity Attribute Reduction: An Acceleration Approach

2020 ◽  
Author(s):  
Jingjing Song ◽  
Huili Dou ◽  
Xiansheng Rao ◽  
Xiaojing Luo ◽  
Xuan Yan
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Superior Performance ◽  
Data Sets ◽  
Feature Selection Technique ◽  
Elapsed Time ◽  
Multiple Granularities ◽  
Naive Approach

As a feature selection technique in rough set theory, attribute reduction has been extensively explored from various viewpoints especially the aspect of granularity, and multi-granularity attribute reduction has attracted much attention. Nevertheless, it should be pointed out that multiple granularities require to be considered simultaneously to evaluate the significance of candidate attribute in the corresponding process of computing reduct, which may result in high elapsed time of searching reduct. To alleviate such a problem, an acceleration strategy for neighborhood based multi-granularity attribute reduction is proposed in this paper, which aims to improve the computational efficiency of searching reduct. Our proposed approach is actually realized through the positive approximation mechanism, and the processes of searching qualified attributes are executed through evaluating candidate attributes over the gradually reduced sample space rather than all samples. The experimental results over 12 UCI data sets demonstrate that the acceleration strategy can provide superior performance to the naive approach of deriving multi-granularity reduct in the elapsed time of computing reduct without generating different reducts.

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