A new soft rough set parameter reduction method for an effective decision-making

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F17%3A50013682" target="_blank" >RIV/62690094:18450/17:50013682 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.3233/978-1-61499-800-6-691" target="_blank" >http://dx.doi.org/10.3233/978-1-61499-800-6-691</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3233/978-1-61499-800-6-691" target="_blank" >10.3233/978-1-61499-800-6-691</a>

Result language

angličtina

Original language name

A new soft rough set parameter reduction method for an effective decision-making

Original language description

Decision-making involves several processes such as data pre-processing, data reduction and data selection. In order to assure a valuable solution is made, each of these processes needs to be successfully conducted. When dealing with complex data, parameter reduction is one of the essential processes that the decision-makers should take into account. It helps to reduce the processing time, computational memory and data dimensionality in the decision-making process. However, some of the parameter reduction methods were unable to generate a suboptimal value during the parameter reduction process. This problem could affect the performance of the classification process. Soft set theory is one of the parameter reduction methods that faces this kind of problem. As a result of the study, to enhance the capability of soft set parameter reduction method, an integration between soft set and rough set theories as a parameter reduction method had been proposed. It was based on the efficiency of these two theories in processing complex and uncertain data problems. These two methods were sequentially applied to simplify the initial parameters in order to improve the performance of the classification process. The experimental work had returned positive classification results and successfully assisted the standard soft set parameter reduction method in generating sub-optimal reduction set and also the classifier in the classification process.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Frontiers in Artificial Intelligence and Applications

ISBN

978-1-61499-799-3

ISSN

0922-6389

e-ISSN

neuvedeno

Number of pages

14

Pages from-to

691-704

Publisher name

IOS press

Place of publication

Amsterdam

Event location

Kitakyushu; Japan

Event date

Sep 26, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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