Toward Qualitative Assessment of Rough Sets in Terms of Decision Attribute Values in Simple Decision Systems over Ontological Graphs

2015 ◽  
pp. 83-94 ◽  
Author(s):  
Krzysztof Pancerz
Keyword(s):  
Rough Sets ◽  
Qualitative Assessment ◽  
Decision Systems

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.

scholarly journals Hybrid Multilabel Feature Selection Using BPSO and Neighborhood Rough Sets for Multilabel Neighborhood Decision Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 175793-175815 ◽  
Author(s):  
Lin Sun ◽  
Tengyu Yin ◽  
Weiping Ding ◽  
Jiucheng Xu
Keyword(s):  
Feature Selection ◽  
Rough Sets ◽  
Decision Systems ◽  
Neighborhood Rough Sets

A new approach of attribute reduction of rough sets based on soft metric

2020 ◽  
Vol 39 (3) ◽  
pp. 4473-4489
Author(s):  
H.I. Mustafa ◽  
O.A. Tantawy
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Attribute Reduction ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Data Set ◽  
Decision Systems ◽  
Proposed Model ◽  
Processing Step ◽  
Important Processing

Attribute reduction is considered as an important processing step for pattern recognition, machine learning and data mining. In this paper, we combine soft set and rough set to use them in applications. We generalize rough set model and introduce a soft metric rough set model to deal with the problem of heterogeneous numerical feature subset selection. We construct a soft metric on the family of knowledge structures based on the soft distance between attributes. The proposed model will degrade to the classical one if we specify a zero soft real number. We also provide a systematic study of attribute reduction of rough sets based on soft metric. Based on the constructed metric, we define co-information systems and consistent co-decision systems, and we provide a new method of attribute reductions of each system. Furthermore, we present a judgement theorem and discernibility matrix associated with attribute of each type of system. As an application, we present a case study from Zoo data set to verify our theoretical results.

A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets

2007 ◽  
Vol 177 (17) ◽  
pp. 3500-3518 ◽  
Author(s):  
Chen Degang ◽  
Wang Changzhong ◽  
Hu Qinghua
Keyword(s):  
Rough Sets ◽  
Attribute Reduction ◽  
New Approach ◽  
Decision Systems

A Heuristic Algorithm for Decision Table Reduction

2012 ◽  
Vol 532-533 ◽  
pp. 1543-1547
Author(s):  
Na Su ◽  
Feng Feng Liao ◽  
Zhe Hui Wu
Keyword(s):  
Heuristic Algorithm ◽  
Information Entropy ◽  
Rough Sets ◽  
Decision Table ◽  
Chi Square ◽  
Decision Systems

The independency between two attribute subsets can be verified based on Chi square statistic to reduce candidate sets. Based on this measure, heuristic algorithm employing information entropy for reduction of decision systems is presented by combining rough sets and statistics. And the validity of this algorithm is analyzed.

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.

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