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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

Frontiers in Artificial Intelligence and Applications

ISBN

978-1-61499-799-3

ISSN

0922-6389

e-ISSN

neuvedeno

Počet stran výsledku

14

Strana od-do

691-704

Název nakladatele

IOS press

Místo vydání

Amsterdam

Místo konání akce

Kitakyushu; Japan

Datum konání akce

26. 9. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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