Publication:
Random Undersampling on Imbalance Time Series Data for Anomaly Detection

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dc.citedby2
dc.contributor.authorSaripuddin M.en_US
dc.contributor.authorSuliman A.en_US
dc.contributor.authorSyarmila Sameon S.en_US
dc.contributor.authorJorgensen B.N.en_US
dc.contributor.authorid57220806580en_US
dc.contributor.authorid25825739000en_US
dc.contributor.authorid36683226000en_US
dc.contributor.authorid7202434812en_US
dc.date.accessioned2023-05-29T09:05:57Z
dc.date.available2023-05-29T09:05:57Z
dc.date.issued2021
dc.descriptionDeep 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 detectionen_US
dc.description.abstractRandom 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1145/3490725.3490748
dc.identifier.epage156
dc.identifier.scopus2-s2.0-85122640323
dc.identifier.spage151
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85122640323&doi=10.1145%2f3490725.3490748&partnerID=40&md5=45679e452d6964071e883c10306c7cd7
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25993
dc.publisherAssociation for Computing Machineryen_US
dc.sourceScopus
dc.sourcetitleACM International Conference Proceeding Series
dc.titleRandom Undersampling on Imbalance Time Series Data for Anomaly Detectionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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