Improving class noise detection and classification performance: A new two-filter CNDC model
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Improving class noise detection and classification performance: A new two-filter CNDC model
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied soft computing
ISSN
1568-4946
e-ISSN
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Volume of the periodical
94
Issue of the periodical within the volume
September
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
16
Pages from-to
"Article Number: 106428"
UT code for WoS article
000565708100011
EID of the result in the Scopus database
2-s2.0-85086442420