Random Undersampling on Imbalance Time Series Data for Anomaly Detection

Mulyana Saripuddin, Azizah Suliman, Sera Syarmila Bt Sameon, Bo Nørregaard Jørgensen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationMLMI'21: 2021 The 4th International Conference on Machine Learning and Machine Intelligence
PublisherAssociation for Computing Machinery
Publication date17. Sept 2021
Pages151–156
ISBN (Print)9781450384247
ISBN (Electronic)9781450384247
DOIs
Publication statusPublished - 17. Sept 2021
EventThe 4th International Conference on Machine Learning and Machine Intelligence. Virtual -
Duration: 17. Sept 202119. Sept 2021

Conference

ConferenceThe 4th International Conference on Machine Learning and Machine Intelligence. Virtual
Period17/09/202119/09/2021

Keywords

  • Anomaly detection
  • Electricity theft detection
  • Imbalance time series data
  • Machine learning
  • Oversampling
  • Undersampling

Fingerprint

Dive into the research topics of 'Random Undersampling on Imbalance Time Series Data for Anomaly Detection'. Together they form a unique fingerprint.

Cite this