Random Undersampling on Imbalance Time Series Data for Anomaly Detection

Saripuddin, Mulyana; Suliman, Azizah; Sameon, Sera Syarmila; Jôrgensen, Bo Nørregaard

doi:10.1145/3490725.3490748

Cited by 18 publications

(11 citation statements)

References 2 publications

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“…Random undersampling (RUS) is a technique used in machine learning to address the issue of imbalanced datasets, where the majority class signi cantly outnumbers the minority class (Kamei et al, 2007;Saripuddin et al, 2021;Zuech et al, 2021). This approach involves randomly selecting a subset of samples from the majority class and removing them from the dataset to achieve a more balanced class distribution.…”

Section: Random Undersamplingmentioning

confidence: 99%

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Exploring Resampling Techniques in Credit Card Default Prediction

Lokanan

2024

Preprint

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In the field of machine learning, the preparation of data is a pivotal step in optimizing model performance. This paper delves into the crucial role of data cleaning and transformation, with a particular emphasis on resampling techniques tailored for addressing imbalanced datasets. By emphasizing the significance of tailored data preparation methodologies, this study underscores the role of resampling techniques in optimizing model performance, especially when dealing with imbalanced datasets. Through an exploration of both undersampling and oversampling methods, the study delves into their nuanced impacts on classification performance and explores the potential trade-offs inherent in each approach. Focusing on the domain of credit card default prediction, the research leverages the UCI Credit Card dataset to provide a comprehensive analysis. The results demonstrate that NearMiss outperformed other undersampling techniques across all classifiers and evaluation metrics. Similarly, K-MeansSMOTE emerged as the top-performing oversampling technique across all classifiers and evaluation metrics. Among the techniques investigated in the study, K-MeansSMOTE oversampling yielded the highest performance accuracy. The findings from this paper enhance our understanding of the performance of different resampling techniques and contribute to the scholarship on handling imbalanced datasets. The results show the pros and cons of different resampling methods used with different machine learning algorithms. They also show how important customized methods are for getting accurate predictions. While offering valuable insights, the study acknowledges the necessity for further research to refine and generalize these techniques across diverse domains and real-world applications, thereby contributing to the broader landscape of machine learning methodologies.

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Section: Random Undersamplingmentioning

confidence: 99%

“…Recent studies on RUS in machine learning span across various domains, each exploring the technique's effectiveness in addressing class imbalance problems in speci c domains. Some researchers have employed RUS to improve classi cation performance in anomaly detection (Huan et al, 2020;Saripuddin et al, 2021; Y. Yang et al, 2023).…”

Section: Random Undersamplingmentioning

confidence: 99%

Exploring Resampling Techniques in Credit Card Default Prediction

Lokanan

2024

Preprint

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“…The sampling techniques used include random undersampling, random oversampling, and Synthetic Minority Oversampling Technique (SMOTE). In the random undersampling technique, data in the majority class is reduced by selecting randomly so that the amount is the same as data in the minority class [18], [19]. Meanwhile, in the random oversampling technique, data in the minority class is duplicated by selecting data randomly so that the amount is the same as data in the majority class [20].…”

Section: Data Sampling Techniquementioning

confidence: 99%

Analysis of Data and Feature Processing on Stroke Prediction using Wide Range Machine Learning Model

Wisesty,

Wirayuda,

Sthevanie

et al. 2024

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Stroke is a disease which cause the death of brain cells, so that the part of the body controlled by the brain loses its function. If not treated immediately, this disease can cause long-term disability, brain damage, and death. In this research, stroke prediction was carried out on the Stroke dataset acquired from the Kaggle dataset using various machine learning models. Then, data sampling techniques are used to handle data imbalance problems in the stroke dataset, which include Random Undersampling, Random Oversampling, and SMOTE techniques. Pearson Correlation and Principal Component Analysis are also used for dimensional reduction and analyzing the important features that are most influential in predicting stroke. Pearson Correlation produces five attributes that have the highest Pearson coefficient, namely age, hypertension, heart disease, blood sugar level, and marital status. Experimental results have demonstrated that the utilization of RUS, ROS, and SMOTE sampling techniques can significantly boost the F1-Score testing by an impressive 43.44%, 34.44%, and 35.55% respectively, as compared to experiments conducted without implementing any data sampling techniques. The highest F1-Score testing was achieved using the Support Vector Machine and Gaussian Naïve Bayes models, namely 0.83.

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“…Additionally, lagged signals and other measures such as rate of change, first-and second-order difference, etc., were also employed. Due to the imbalanced nature of the given ML task, random undersampling [9][10][11] was performed to balance the distribution of the target labels. As a result, the final dataset was generated.…”

Section: Data Descriptionmentioning

confidence: 99%

Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion

Arnaut,

Kolarski,

Srećković

2024

Data

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Machine learning (ML) methods are commonly applied in the fields of extraterrestrial physics, space science, and plasma physics. In a prior publication, an ML classification technique, the Random Forest (RF) algorithm, was utilized to automatically identify and categorize erroneous signals, including instrument errors, noisy signals, outlier data points, and the impact of solar flares (SFs) on the ionosphere. This data communication includes the pre-processed dataset used in the aforementioned research, along with a workflow that utilizes the PyCaret library and a post-processing workflow. The code and data serve educational purposes in the interdisciplinary field of ML and ionospheric physics science, as well as being useful to other researchers for diverse objectives.

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Random Undersampling on Imbalance Time Series Data for Anomaly Detection

Cited by 18 publications

References 2 publications

Exploring Resampling Techniques in Credit Card Default Prediction

Exploring Resampling Techniques in Credit Card Default Prediction

Analysis of Data and Feature Processing on Stroke Prediction using Wide Range Machine Learning Model

Machine Learning Classification Workflow and Datasets for Ionospheric VLF Data Exclusion

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