[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120184-en":3,"doc-seo-120184-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120184,1099514068035,"Ezra","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Anomaly detection using unsupervised machine learning algorithms: A simulation study","This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope). Through systematic analysis on a synthetically simulated dataset, the study assessed each algorithm’s predictive performance using accuracy, precision, recall, and F1 score specifically for outlier detection. The evaluation reveals that One-Class SVM, Isolation Forest, and Robust Covariance are more effective in identifying outliers in the synthetic simulated dataset, with Isolation Forest slightly outperforming the other algorithms in terms of balancing precision and recall. One-Class SVM with SGD shows promise in precision but needs adjustment to improve recall. Local Outlier Factor may require parameter tuning or may not be as suitable for this particular dataset’s characteristics. The findings reveal significant variations in performance, highlighting the strengths and limitations of each method in identifying anomalies. This research contributes to the field of machine learning by demonstrating that the selection of an anomaly detection algorithm should be a considered decision, taking into account the specific characteristics of the data and the operational context of its application. Future work should explore parameter optimization, the impact of dataset characteristics on model performance, and the application of these models to real-world datasets to validate their efficacy in practical anomaly detection scenarios.","Scientiϧc African 26 (2024) e02386  \n| Anomaly detection using unsupervised machine learning algorithms: A simulation study\u003Cbr>Edmund Fosu Agyemang ∗\u003Cbr>School of Mathematical and Statistical Science, College of Sciences, University of Texas Rio Grande Valley, USA Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Ghana Department of Computer Science, Ashesi University, No. 1 University Avenue, Berekuso, Accra, Ghana |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O\u003Cbr>Editor name: DR B Gyampoh\u003Cbr>Keywords:\u003Cbr>Anomaly detection\u003Cbr>Unsupervised machine learning algorithms One-class support vector machine Isolation forest\u003Cbr>Local outlier factor\u003Cbr>Robust covariance | A B S T R A C T\u003Cbr>This study presents a comprehensive evaluation of five prominent unsupervised machine learning anomaly detection algorithms: One-Class Support Vector Machine (One-Class SVM), One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope). Through systematic analysis on a synthetically simulated dataset, the study assessed each algorithm’s predictive performance using accuracy, precision, recall, and F1 score specifically for outlier detection. The evaluation reveals that One-Class SVM, Isolation Forest, and Robust Covariance are more effective in identifying outliers in the synthetic simulated dataset, with Isolation Forest slightly outperforming the other algorithms in terms of balancing precision and recall. One-Class SVM with SGD shows promise in precision but needs adjustment to improve recall. Local Outlier Factor may require parameter tuning or may not be as suitable for this particular dataset’s characteristics. The findings reveal significant variations in performance, highlighting the strengths and limitations of each method in identifying anomalies. This research contributes to the field of machine learning by demonstrating that the selection of an anomaly detection algorithm should be a considered decision, taking into account the specific characteristics of the data and the operational context of its application. Future work should explore parameter optimization, the impact of dataset characteristics on model performance, and the application of these models to real-world datasets to validate their efficacy in practical anomaly detection scenarios. |  |\n\nIntroduction  \nAnomaly detection, a critical component of data analysis, plays a pivotal role in identifying irregularities that deviate from normal patterns in datasets [1]. In the era of digital transformation, the ability to automatically identify unusual patterns or anomalies in data has become increasingly crucial across various sectors, including finance, healthcare, cybersecurity, and manufacturing. Anomalies can indicate significant, often critical, information ranging from fraudulent transactions to malfunctioning equipment. The challenge, however, lies in detecting these irregularities, especially when the definition of ‘normal’ is constantly evolving and the nature of anomalies can be highly unpredictable. Traditional anomaly detection methods, which often rely on predefined thresholds or specific assumptions about data distribution, are increasingly inadequate due to their lack of flexibility and scalability. These anomalies can indicate significant, often critical, actionable insights across various domains. In cybersecurity, anomaly detection systems identify unusual patterns that may signify security breaches, such as unauthorized access or malware activities [2]. In the  \n∗ Correspondence to: Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Ghana.  \nE-mail address: [edmundfosu6@gmail.com](edmundfosu6@gmail.com).  \n[https://doi.org/10.1016/j.sciaf.2024.e02386](https://doi.org/10.1016/j.sciaf.2024.e02386)  \nReceived 15 February 2024; Received in revised form 6 Septembe","cbCainbXAdi9Eydi","https://ap.wps.com/l/cbCainbXAdi9Eydi","pdf",2075529,1,16,"English","en",105,"# Introduction\n## Rationale and problem context of anomaly detection\n## Limitations of traditional statistical approaches\n## Shift to machine learning and unsupervised methods\n## Motivation across key application domains","[{\"question\":\"Which unsupervised anomaly detection algorithms are evaluated in the study?\",\"answer\":\"The study evaluates One-Class SVM, One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope).\"},{\"question\":\"How is algorithm performance measured for outlier detection?\",\"answer\":\"Performance is assessed using accuracy, precision, recall, and the F1 score on a synthetically simulated dataset focused on outlier detection.\"},{\"question\":\"What main conclusion does the study reach about algorithm selection?\",\"answer\":\"Results show that effectiveness varies significantly by method; the study concludes that choosing an anomaly detection algorithm should consider data characteristics and the operational context, and not rely on a single universally best approach.\"}]","Anomaly detection using unsupervised machine learning algorithms: A simulation study | PDF",1785728597,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"anomaly-detection-using-unsupervised-machine-learning-algorithms-a-simulation-study","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/anomaly-detection-using-unsupervised-machine-learning-algorithms-a-simulation-study/120184/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which unsupervised anomaly detection algorithms are evaluated in the study?","Question",{"text":75,"@type":76},"The study evaluates One-Class SVM, One-Class SVM with Stochastic Gradient Descent (SGD), Isolation Forest (iForest), Local Outlier Factor (LOF), and Robust Covariance (Elliptic Envelope).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is algorithm performance measured for outlier detection?",{"text":80,"@type":76},"Performance is assessed using accuracy, precision, recall, and the F1 score on a synthetically simulated dataset focused on outlier detection.",{"name":82,"@type":73,"acceptedAnswer":83},"What main conclusion does the study reach about algorithm selection?",{"text":84,"@type":76},"Results show that effectiveness varies significantly by method; the study concludes that choosing an anomaly detection algorithm should consider data characteristics and the operational context, and not rely on a single universally best approach.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]