Analysis on Hybrid Dominance-Based Rough Set Parameterization Using Private Financial Initiative Unitary Charges Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F18%3A50014745" target="_blank" >RIV/62690094:18450/18:50014745 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-319-75417-8_30" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-75417-8_30</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-75417-8_30" target="_blank" >10.1007/978-3-319-75417-8_30</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Analysis on Hybrid Dominance-Based Rough Set Parameterization Using Private Financial Initiative Unitary Charges Data

Popis výsledku v původním jazyce

This paper evaluates the capability of the hybrid parameter reduction approach in handling private financial initiative (PFI) unitary charges data to increase the classification performance. The objective of this study is to analyse the performance of the proposed hybrid parameter reduction approach in assisting the neural network classifier to classify complex data sets that might contain uncertain and inconsistent problems. The proposed hybrid parameter reduction approach consists of several methods that will be executed during the data analysis process. Slicing technique and dominance-based rough set approach (DRSA) are the two techniques that play important roles in the proposed parameter reduction process. In order, to analyse the performance of the proposed work, the PFI data that covers all regions in Malaysia is applied in the experimental works. Besides, several standard data sets have also been used to validate the obtained results. The results reveal that the hybrid approach has successfully assisted the classifier in the classification process.

Název v anglickém jazyce

Analysis on Hybrid Dominance-Based Rough Set Parameterization Using Private Financial Initiative Unitary Charges Data

Popis výsledku anglicky

This paper evaluates the capability of the hybrid parameter reduction approach in handling private financial initiative (PFI) unitary charges data to increase the classification performance. The objective of this study is to analyse the performance of the proposed hybrid parameter reduction approach in assisting the neural network classifier to classify complex data sets that might contain uncertain and inconsistent problems. The proposed hybrid parameter reduction approach consists of several methods that will be executed during the data analysis process. Slicing technique and dominance-based rough set approach (DRSA) are the two techniques that play important roles in the proposed parameter reduction process. In order, to analyse the performance of the proposed work, the PFI data that covers all regions in Malaysia is applied in the experimental works. Besides, several standard data sets have also been used to validate the obtained results. The results reveal that the hybrid approach has successfully assisted the classifier in the classification process.

Druh

D - Stať ve sborníku

CEP obor

—

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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

INTELLIGENT INFORMATION AND DATABASE SYSTEMS, ACIIDS 2018, PT I

ISBN

978-3-319-75417-8

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

11

Strana od-do

318-328

Název nakladatele

SPRINGER INTERNATIONAL PUBLISHING AG

Místo vydání

CHAM

Místo konání akce

Dong Hoi

Datum konání akce

19. 3. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000432717700030