Staff View: Random Undersampling on Imbalance Time Series Data for Anomaly Detection

Random Undersampling on Imbalance Time Series Data for Anomaly Detection

Deep learning; Learning algorithms; Time series; Anomaly detection; Electricity theft detection; Imbalance datum; Imbalance time series data; Over sampling; Overfitting; Random under samplings; Resampling approaches; Time-series data; Under-sampling; Anomaly detection

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Bibliographic Details
Main Authors:	Saripuddin M., Suliman A., Syarmila Sameon S., Jorgensen B.N.
Other Authors:	57220806580
Format:	Conference Paper
Published:	Association for Computing Machinery 2023
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id	my.uniten.dspace-25993
record_format	dspace
spelling	my.uniten.dspace-259932023-05-29T17:05:57Z Random Undersampling on Imbalance Time Series Data for Anomaly Detection Saripuddin M. Suliman A. Syarmila Sameon S. Jorgensen B.N. 57220806580 25825739000 36683226000 7202434812 Deep learning; Learning algorithms; Time series; Anomaly detection; Electricity theft detection; Imbalance datum; Imbalance time series data; Over sampling; Overfitting; Random under samplings; Resampling approaches; Time-series data; Under-sampling; Anomaly detection Random Undersampling (RUS) is one of resampling approaches to tackle issues with imbalance data by removing instances randomly from the majority class. Anomaly is considered as a rare case, thus the number of instances in the anomaly class is usually much lower than instances in other classes. In anomaly detection of time series data, an anomaly is identified when an unusual pattern exists. Duplicating the unusual pattern may lead to overfitting, which is why this study considered an undersampling method over oversampling approach. This study applied RUS on data with several algorithms to observe its effectiveness on different types of classifier. To prove the overfitting and underfitting issues, different ratios of training and testing were used. Five different evaluation metrics were considered to evaluate the performance of the approach used. It was found that RUS could improve the classification performance of every classifier and the best result was shown when RUS was applied on a deep learning algorithm. � 2021 ACM. Final 2023-05-29T09:05:57Z 2023-05-29T09:05:57Z 2021 Conference Paper 10.1145/3490725.3490748 2-s2.0-85122640323 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85122640323&doi=10.1145%2f3490725.3490748&partnerID=40&md5=45679e452d6964071e883c10306c7cd7 https://irepository.uniten.edu.my/handle/123456789/25993 151 156 Association for Computing Machinery Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
description	Deep learning; Learning algorithms; Time series; Anomaly detection; Electricity theft detection; Imbalance datum; Imbalance time series data; Over sampling; Overfitting; Random under samplings; Resampling approaches; Time-series data; Under-sampling; Anomaly detection
author2	57220806580
author_facet	57220806580 Saripuddin M. Suliman A. Syarmila Sameon S. Jorgensen B.N.
format	Conference Paper
author	Saripuddin M. Suliman A. Syarmila Sameon S. Jorgensen B.N.
spellingShingle	Saripuddin M. Suliman A. Syarmila Sameon S. Jorgensen B.N. Random Undersampling on Imbalance Time Series Data for Anomaly Detection
author_sort	Saripuddin M.
title	Random Undersampling on Imbalance Time Series Data for Anomaly Detection
title_short	Random Undersampling on Imbalance Time Series Data for Anomaly Detection
title_full	Random Undersampling on Imbalance Time Series Data for Anomaly Detection
title_fullStr	Random Undersampling on Imbalance Time Series Data for Anomaly Detection
title_full_unstemmed	Random Undersampling on Imbalance Time Series Data for Anomaly Detection
title_sort	random undersampling on imbalance time series data for anomaly detection
publisher	Association for Computing Machinery
publishDate	2023
_version_	1806427614324195328
score	13.222552

Random Undersampling on Imbalance Time Series Data for Anomaly Detection

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