Combining rough set-based relevance and redundancy for the ranking and selection of nominal features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F20%3A39916680" target="_blank" >RIV/00216275:25410/20:39916680 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1877050920320561" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1877050920320561</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2020.09.156" target="_blank" >10.1016/j.procs.2020.09.156</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Combining rough set-based relevance and redundancy for the ranking and selection of nominal features

Popis výsledku v původním jazyce

In this paper, we propose a new method for features ranking and selection. Our approach is based on ranking nominal features in terms of their relevance to the assigned class and mutual redundancy with the other features. To calculate the relevance and redundancy, we propose to use a rough-set based approach. After performing the ranking, features filtering is carried out in a supervised way enabling the user to decide on the number of the retained features. The experiments revealed that thanks to our method, it is possible to filter out numerous features describing data while still maintaining satisfactory classification accuracy achieved by the classifier trained using the reduced dataset. The comparative experiments performed with the use of publicly available datasets proved the high efficiency and competitiveness of our approach.

Název v anglickém jazyce

Combining rough set-based relevance and redundancy for the ranking and selection of nominal features

Popis výsledku anglicky

In this paper, we propose a new method for features ranking and selection. Our approach is based on ranking nominal features in terms of their relevance to the assigned class and mutual redundancy with the other features. To calculate the relevance and redundancy, we propose to use a rough-set based approach. After performing the ranking, features filtering is carried out in a supervised way enabling the user to decide on the number of the retained features. The experiments revealed that thanks to our method, it is possible to filter out numerous features describing data while still maintaining satisfactory classification accuracy achieved by the classifier trained using the reduced dataset. The comparative experiments performed with the use of publicly available datasets proved the high efficiency and competitiveness of our approach.

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

<a href="/cs/project/GA19-15498S" target="_blank" >GA19-15498S: Modelování emocí ve verbální a neverbální manažerské komunikaci pro predikci podnikových finančních rizik</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

Procedia Computer Science : 24th KES International Conference on Knowledge-Based and Intelligent Information &amp; Engineering Systems KES2020

ISBN

—

ISSN

1877-0509

e-ISSN

—

Počet stran výsledku

10

Strana od-do

1459-1468

Název nakladatele

Elsevier Science BV

Místo vydání

Amsterdam

Místo konání akce

ONLINE

Datum konání akce

16. 9. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—