A hybrid model for class noise detection using k-means and classification filtering algorithms

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F20%3A50017131" target="_blank" >RIV/62690094:18450/20:50017131 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/article/10.1007/s42452-020-3129-x" target="_blank" >https://link.springer.com/article/10.1007/s42452-020-3129-x</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s42452-020-3129-x" target="_blank" >10.1007/s42452-020-3129-x</a>

Result language

angličtina

Original language name

A hybrid model for class noise detection using k-means and classification filtering algorithms

Original language description

Real data may have a considerable amount of noise produced by error in data collection, transmission and storage. The noisy training data set increases the training time and complexity of the induced machine learning model, which led to reduce the overall performance. Identifying noisy instances and then eliminating or correcting them are useful techniques in data mining research. This paper investigates misclassified instances issues and proposes a clustering-based and classification filtering algorithm (CLCF) in noise detection and classification model. It applies the k-means clustering technique for noise detection, and then five different classification filtering algorithms are applied for noise filtering. It also employs two well-known techniques for noise classification, namely, removing and relabeling. To evaluate the performance of the CLCF model, several experiments were conducted on four binary data sets.The proposed technique was found to be successful in classify class noisy instances, which is significantly effective for decision making system in several domains such as medical areas. The results shows that the proposed model led to a significant performance improvement compared with before performing noise filtering.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10103 - Statistics and probability

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

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

Name of the periodical

SN APPLIED SCIENCES

ISSN

2523-3963

e-ISSN

—

Volume of the periodical

2

Issue of the periodical within the volume

7

Country of publishing house

CH - SWITZERLAND

Number of pages

10

Pages from-to

"Article Number: 1303"

UT code for WoS article

000548070900004

EID of the result in the Scopus database

2-s2.0-85100707341