ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION

The extremely skewed data in artificial intelligence, machine learning, and data mining cases are often given misleading results. It is caused because machine learning algorithms are designated to work best with balanced data. However, we often meet with imbalanced data in the real situation. To han...

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Autores principales:	Ariani Indrawati, Hendro Subagyo, Andre Sihombing, Wagiyah Wagiyah, Sjaeful Afandi
Formato:	article
Lenguaje:	EN ID
Publicado:	Lembaga Ilmu Pengetahuan Indonesia 2020
Materias:	imbalanced data resampling techniques machine learning classification journal isjd Bibliography. Library science. Information resources Z
Acceso en línea:	https://doaj.org/article/bf6a90b54cc34ae89e9eee9d4ad072ff
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spelling	oai:doaj.org-article:bf6a90b54cc34ae89e9eee9d4ad072ff2021-12-02T18:49:59ZANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION0125-90082301-859310.14203/j.baca.v41i2.702https://doaj.org/article/bf6a90b54cc34ae89e9eee9d4ad072ff2020-12-01T00:00:00Zhttps://jurnalbaca.pdii.lipi.go.id/index.php/baca/article/view/702https://doaj.org/toc/0125-9008https://doaj.org/toc/2301-8593The extremely skewed data in artificial intelligence, machine learning, and data mining cases are often given misleading results. It is caused because machine learning algorithms are designated to work best with balanced data. However, we often meet with imbalanced data in the real situation. To handling imbalanced data issues, the most popular technique is resampling the dataset to modify the number of instances in the majority and minority classes into a standard balanced data. Many resampling techniques, oversampling, undersampling, or combined both of them, have been proposed and continue until now. Resampling techniques may increase or decrease the classifier performance. Comparative research on resampling methods in structured data has been widely carried out, but studies that compare resampling methods with unstructured data are very rarely conducted. That raises many questions, one of which is whether this method is applied to unstructured data such as text that has large dimensions and very diverse characters. To understand how different resampling techniques will affect the learning of classifiers for imbalanced data text, we perform an experimental analysis using various resampling methods with several classification algorithms to classify articles at the Indonesian Scientific Journal Database (ISJD). From this experiment, it is known resampling techniques on imbalanced data text generally to improve the classifier performance but they are doesn’t give significant result because data text has very diverse and large dimensions.Ariani IndrawatiHendro SubagyoAndre SihombingWagiyah WagiyahSjaeful AfandiLembaga Ilmu Pengetahuan Indonesiaarticleimbalanced dataresampling techniquesmachine learningclassificationjournalisjdBibliography. Library science. Information resourcesZENIDBaca: Jurnal Dokumentasi dan Informasi, Vol 41, Iss 2, Pp 133-141 (2020)
institution	DOAJ
collection	DOAJ
language	EN ID
topic	imbalanced data resampling techniques machine learning classification journal isjd Bibliography. Library science. Information resources Z
spellingShingle	imbalanced data resampling techniques machine learning classification journal isjd Bibliography. Library science. Information resources Z Ariani Indrawati Hendro Subagyo Andre Sihombing Wagiyah Wagiyah Sjaeful Afandi ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION
description	The extremely skewed data in artificial intelligence, machine learning, and data mining cases are often given misleading results. It is caused because machine learning algorithms are designated to work best with balanced data. However, we often meet with imbalanced data in the real situation. To handling imbalanced data issues, the most popular technique is resampling the dataset to modify the number of instances in the majority and minority classes into a standard balanced data. Many resampling techniques, oversampling, undersampling, or combined both of them, have been proposed and continue until now. Resampling techniques may increase or decrease the classifier performance. Comparative research on resampling methods in structured data has been widely carried out, but studies that compare resampling methods with unstructured data are very rarely conducted. That raises many questions, one of which is whether this method is applied to unstructured data such as text that has large dimensions and very diverse characters. To understand how different resampling techniques will affect the learning of classifiers for imbalanced data text, we perform an experimental analysis using various resampling methods with several classification algorithms to classify articles at the Indonesian Scientific Journal Database (ISJD). From this experiment, it is known resampling techniques on imbalanced data text generally to improve the classifier performance but they are doesn’t give significant result because data text has very diverse and large dimensions.
format	article
author	Ariani Indrawati Hendro Subagyo Andre Sihombing Wagiyah Wagiyah Sjaeful Afandi
author_facet	Ariani Indrawati Hendro Subagyo Andre Sihombing Wagiyah Wagiyah Sjaeful Afandi
author_sort	Ariani Indrawati
title	ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION
title_short	ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION
title_full	ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION
title_fullStr	ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION
title_full_unstemmed	ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION
title_sort	analyzing the impact of resampling method for imbalanced data text in indonesian scientific articles categorization
publisher	Lembaga Ilmu Pengetahuan Indonesia
publishDate	2020
url	https://doaj.org/article/bf6a90b54cc34ae89e9eee9d4ad072ff
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ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION

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