Rough Sets and Data Dependencies

Feature selection aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. Rough set theory (RST) has been used as such a tool with much success. RST enables the discovery of data dependencies and the reduction of the number of attributes contained in a dataset using the data alone, requiring no additional information. This chapter describes the fundamental ideas behind RST-based approaches and reviews related feature selection methods that build on these ideas. Extensions to the traditional rough set approach are discussed, including recent selection methods based on tolerance rough sets, variable precision rough sets and fuzzy-rough sets. Alternative search mechanisms are also highly important in rough set feature selection. The chapter includes the latest developments in this area, including RST strategies based on hill-climbing, genetic algorithms and ant colony optimization.

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Data Reduction with Rough Sets

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch087 ◽

2011 ◽

pp. 556-560 ◽

Cited By ~ 1

Author(s):

Richard Jensen

Keyword(s):

Feature Selection ◽

Knowledge Discovery ◽

Set Theory ◽

Rough Set ◽

Data Reduction ◽

Rough Sets ◽

Rough Set Theory ◽

Feature Subset ◽

Data Dependencies ◽

Additional Information

Data reduction is an important step in knowledge discovery from data. The high dimensionality of databases can be reduced using suitable techniques, depending on the requirements of the data mining processes. These techniques fall in to one of the following categories: those that transform the underlying meaning of the data features and those that are semantics-preserving. Feature selection (FS) methods belong to the latter category, where a smaller set of the original features is chosen based on a subset evaluation function. The process aims to determine a minimal feature subset from a problem domain while retaining a suitably high accuracy in representing the original features. In knowledge discovery, feature selection methods are particularly desirable as they facilitate the interpretability of the resulting knowledge. For this, rough set theory has been successfully used as a tool that enables the discovery of data dependencies and the reduction of the number of features contained in a dataset using the data alone, while requiring no additional information.

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Presentation of precedents in decision support system knowledge base under diagnosis of portal cranes on the basis of the rough sets theory

Shipbuilding and marine infrastructure ◽

10.15589/smi20140109 ◽

2014 ◽

Vol 1 (1) ◽

Author(s):

Igor I. Kovalenko ◽

Anton V. Melnik

Keyword(s):

Decision Support ◽

Decision Support System ◽

Knowledge Base ◽

Support System ◽

Rough Sets ◽

Rough Sets Theory ◽

Sets Theory ◽

System Knowledge

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BUSINESS FAILURE PREDICTION USING ROUGH SETS: A COMPARISON WITH MULTIVARIATE ANALYSIS TECHNIQUES

FUZZY ECONOMIC REVIEW ◽

10.25102/fer.1999.01.01 ◽

1999 ◽

Vol 04 (01) ◽

Cited By ~ 4

Author(s):

C. Zopounidis ◽

M. Doumpos ◽

R. Slowinski ◽

R. Susmaga ◽

A. I. Dimitras

Keyword(s):

Multivariate Analysis ◽

Rough Sets ◽

Failure Prediction ◽

Business Failure ◽

Analysis Techniques

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Approximation Sets of Rough Sets

Journal of Software ◽

10.3724/sp.j.1001.2012.04226 ◽

2012 ◽

Vol 23 (7) ◽

pp. 1745-1759 ◽

Cited By ~ 14

Author(s):

Qing-Hua ZHANG ◽

Guo-Yin WANG ◽

Yu XIAO

Keyword(s):

Rough Sets

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Analysing diabetes using rough sets

Advances in Mathematics: Scientific Journal ◽

10.37418/amsj.9.2.1 ◽

2020 ◽

pp. 533-538

Author(s):

S. Arjun Raj ◽

M. Vigneshwaran

Keyword(s):

Set Theory ◽

Rough Set ◽

Rough Sets ◽

Rough Set Theory ◽

International Diabetes Federation ◽

Published Data ◽

Lower And Upper Approximations

In this article we use the rough set theory to generate the set of decision concepts in order to solve a medical problem.Based on officially published data by International Diabetes Federation (IDF), rough sets have been used to diagnose Diabetes.The lower and upper approximations of decision concepts and their boundary regions have been formulated here.

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