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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

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

SN APPLIED SCIENCES

ISSN

2523-3963

e-ISSN

—

Svazek periodika

2

Číslo periodika v rámci svazku

7

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

10

Strana od-do

"Article Number: 1303"

Kód UT WoS článku

000548070900004

EID výsledku v databázi Scopus

2-s2.0-85100707341