An attribute reduction method based on core samples set

2012 ◽  
Author(s):  
Eric C.C. Tsang ◽  
Chen-Xia Jin ◽  
De-Gang Chen ◽  
Fa-Chao Li
Keyword(s):  
Reduction Method ◽  
Attribute Reduction ◽  
Core Samples

A high-dimensional attribute reduction method modeling and evaluation based on green economy data: evidence from 15 sub-provincial cities in China

2019 ◽  
Vol 24 (13) ◽  
pp. 9753-9764
Author(s):  
Gang Li ◽  
Jiaxiang Li ◽  
Yunqi Liu ◽  
Juan Liu ◽  
Baofeng Shi ◽  
...  
Keyword(s):  
Reduction Method ◽  
Attribute Reduction ◽  
Green Economy ◽  
High Dimensional

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.

An incremental attribute reduction method for dynamic data mining

2018 ◽  
Vol 465 ◽  
pp. 202-218 ◽  
Author(s):  
Yunge Jing ◽  
Tianrui Li ◽  
Hamido Fujita ◽  
Baoli Wang ◽  
Ni Cheng
Keyword(s):  
Data Mining ◽  
Reduction Method ◽  
Attribute Reduction ◽  
Dynamic Data
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