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

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...

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Main Authors: Nematzadeh, Zahra, Ibrahim, Roliana, Selamat, Ali
Format: Article
Language:English
Published: Springer, Cham 2020
Online Access:http://eprints.utm.my/id/eprint/93683/1/AliSelamat2020_AHybridModelForClassNoiseDetection.pdf
http://eprints.utm.my/id/eprint/93683/
http://dx.doi.org/10.1007/s42452-020-3129-x
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spelling my.utm.936832021-12-31T08:45:20Z http://eprints.utm.my/id/eprint/93683/ A hybrid model for class noise detection using k-means and classification filtering algorithms Nematzadeh, Zahra Ibrahim, Roliana Selamat, Ali 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. Springer, Cham 2020 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/93683/1/AliSelamat2020_AHybridModelForClassNoiseDetection.pdf Nematzadeh, Zahra and Ibrahim, Roliana and Selamat, Ali (2020) A hybrid model for class noise detection using k-means and classification filtering algorithms. SN Applied Sciences, 2 . p. 1303. ISSN 2523-3963 http://dx.doi.org/10.1007/s42452-020-3129-x
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
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.
format Article
author Nematzadeh, Zahra
Ibrahim, Roliana
Selamat, Ali
spellingShingle Nematzadeh, Zahra
Ibrahim, Roliana
Selamat, Ali
A hybrid model for class noise detection using k-means and classification filtering algorithms
author_facet Nematzadeh, Zahra
Ibrahim, Roliana
Selamat, Ali
author_sort Nematzadeh, Zahra
title A hybrid model for class noise detection using k-means and classification filtering algorithms
title_short A hybrid model for class noise detection using k-means and classification filtering algorithms
title_full A hybrid model for class noise detection using k-means and classification filtering algorithms
title_fullStr A hybrid model for class noise detection using k-means and classification filtering algorithms
title_full_unstemmed A hybrid model for class noise detection using k-means and classification filtering algorithms
title_sort hybrid model for class noise detection using k-means and classification filtering algorithms
publisher Springer, Cham
publishDate 2020
url http://eprints.utm.my/id/eprint/93683/1/AliSelamat2020_AHybridModelForClassNoiseDetection.pdf
http://eprints.utm.my/id/eprint/93683/
http://dx.doi.org/10.1007/s42452-020-3129-x
_version_ 1720980109896187904
score 13.223943

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