Improving class noise detection and classification performance: A new two-filter CNDC model

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017130" target="_blank" >RIV/62690094:18450/20:50017130 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S1568494620303689?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494620303689?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2020.106428" target="_blank" >10.1016/j.asoc.2020.106428</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving class noise detection and classification performance: A new two-filter CNDC model

Popis výsledku v původním jazyce

Class noise is an important issue in classification with a lot of potential consequences. It can decrease the overall accuracy and increase the complexity of the induced model. This study investigates ensemble filtering, removing and relabeling noisy instances issues and proposes a new two-filter model for Class Noise Detection and Classification (CNDC). The proposed two-filter CNDC model comprises two major parts, which are noise detection and noise classification. The noise detection part involves ensemble and distance filtering to overcome ensemble issues. In latter part, a Removing-Relabeling (REM-REL) technique is proposed to enhance overall performance of noise classification. To evaluate the performance of the proposed model, several experiments were conducted on six real data sets. The proposed REM-REL technique was found to be successful to classify noisy instances. The final results showed that the proposed model led to a significant performance improvement compared with ensemble filtering. (C) 2020 Elsevier B.V. All rights reserved.

Název v anglickém jazyce

Improving class noise detection and classification performance: A new two-filter CNDC model

Popis výsledku anglicky

Class noise is an important issue in classification with a lot of potential consequences. It can decrease the overall accuracy and increase the complexity of the induced model. This study investigates ensemble filtering, removing and relabeling noisy instances issues and proposes a new two-filter model for Class Noise Detection and Classification (CNDC). The proposed two-filter CNDC model comprises two major parts, which are noise detection and noise classification. The noise detection part involves ensemble and distance filtering to overcome ensemble issues. In latter part, a Removing-Relabeling (REM-REL) technique is proposed to enhance overall performance of noise classification. To evaluate the performance of the proposed model, several experiments were conducted on six real data sets. The proposed REM-REL technique was found to be successful to classify noisy instances. The final results showed that the proposed model led to a significant performance improvement compared with ensemble filtering. (C) 2020 Elsevier B.V. All rights reserved.

Druh

J_imp - Článek v periodiku v databázi Web of Science

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Název periodika

Applied soft computing

ISSN

1568-4946

e-ISSN

—

Svazek periodika

94

Číslo periodika v rámci svazku

September

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

"Article Number: 106428"

Kód UT WoS článku

000565708100011

EID výsledku v databázi Scopus

2-s2.0-85086442420