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 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 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].

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 A DISTANCE BASED INCREMENTAL FILTER-WRAPPER ALGORITHM FOR FINDING REDUCT IN INCOMPLETE DECISION TABLES

2019 ◽  
Vol 57 (4) ◽  
pp. 499
Author(s):  
Nguyen Ba Quang ◽  
Nguyen Long Giang ◽  
Dang Thi Oanh
Keyword(s):  
Rough Set ◽  
Classification Accuracy ◽  
Attribute Reduction ◽  
Computation Time ◽  
Incremental Algorithms ◽  
Multiple Objects ◽  
Tolerance Rough Set ◽  
Decision Tables ◽  
Filter Algorithms ◽  
Reduce Computation Time

Tolerance rough set model is an effective tool for attribute reduction in incomplete decision tables. In recent years, some incremental algorithms have been proposed to find reduct of dynamic incomplete decision tables in order to reduce computation time. However, they are classical filter algorithms, in which the classification accuracy of decision tables is computed after obtaining reduct. Therefore, the obtained reducts of these algorithms are not optimal on cardinality of reduct and classification accuracy. In this paper, we propose the incremental filter-wrapper algorithm IDS_IFW_AO to find one reduct of an incomplete desision table in case of adding multiple objects. The experimental results on some sample datasets show that the proposed filter-wrapper algorithm IDS_IFW_AO is more effective than the filter algorithm IARM-I [17] on classification accuracy and cardinality of reduct

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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