Cellular frustration algorithms for anomaly detection applications
Author
Abstract
Suggested Citation
DOI: 10.1371/journal.pone.0218930
Download full text from publisher
References listed on IDEAS
- Markus Goldstein & Seiichi Uchida, 2016. "A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
- Sabri Boughorbel & Fethi Jarray & Mohammed El-Anbari, 2017. "Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-17, June.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Vinicius Francisco Rofatto & Marcelo Tomio Matsuoka & Ivandro Klein & Maurício Roberto Veronez & Luiz Gonzaga da Silveira Junior, 2020. "On the effects of hard and soft equality constraints in the iterative outlier elimination procedure," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-29, August.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Yin, Sihua & Yang, Haidong & Xu, Kangkang & Zhu, Chengjiu & Zhang, Shaqing & Liu, Guosheng, 2022. "Dynamic real–time abnormal energy consumption detection and energy efficiency optimization analysis considering uncertainty," Applied Energy, Elsevier, vol. 307(C).
- Adele Ravagnani & Fabrizio Lillo & Paola Deriu & Piero Mazzarisi & Francesca Medda & Antonio Russo, 2024. "Dimensionality reduction techniques to support insider trading detection," Papers 2403.00707, arXiv.org, revised May 2024.
- Davide Nicola Continanza & Andrea del Monaco & Marco di Lucido & Daniele Figoli & Pasquale Maddaloni & Filippo Quarta & Giuseppe Turturiello, 2023.
"Stacking machine learning models for anomaly detection: comparing AnaCredit to other banking data sets,"
IFC Bulletins chapters, in: Bank for International Settlements (ed.), Data science in central banking: applications and tools, volume 59,
Bank for International Settlements.
- Pasquale Maddaloni & Davide Nicola Continanza & Andrea del Monaco & Daniele Figoli & Marco di Lucido & Filippo Quarta & Giuseppe Turturiello, 2022. "Stacking machine-learning models for anomaly detection: comparing AnaCredit to other banking datasets," Questioni di Economia e Finanza (Occasional Papers) 689, Bank of Italy, Economic Research and International Relations Area.
- Sevvandi Kandanaarachchi & Mario A Munoz & Rob J Hyndman & Kate Smith-Miles, 2018. "On normalization and algorithm selection for unsupervised outlier detection," Monash Econometrics and Business Statistics Working Papers 16/18, Monash University, Department of Econometrics and Business Statistics.
- Christian Kauten & Ashish Gupta & Xiao Qin & Glenn Richey, 2022. "Predicting Blood Donors Using Machine Learning Techniques," Information Systems Frontiers, Springer, vol. 24(5), pages 1547-1562, October.
- Priyanga Dilini Talagala & Rob J Hyndman & Catherine Leigh & Kerrie Mengersen & Kate Smith-Miles, 2019. "A Feature-Based Framework for Detecting Technical Outliers in Water-Quality Data from In Situ Sensors," Monash Econometrics and Business Statistics Working Papers 1/19, Monash University, Department of Econometrics and Business Statistics.
- Cian Ryan & Finbarr Murphy & Martin Mullins, 2019. "Semiautonomous Vehicle Risk Analysis: A Telematics‐Based Anomaly Detection Approach," Risk Analysis, John Wiley & Sons, vol. 39(5), pages 1125-1140, May.
- Elmira Asadi-Fard & Samereh Falahatkar & Mahdi Tanha Ziyarati & Xiaodong Zhang & Mariapia Faruolo, 2023. "Assessment of RXD Algorithm Capability for Gas Flaring Detection through OLI-SWIR Channels," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
- Kenichiro Nagata & Toshikazu Tsuji & Kimitaka Suetsugu & Kayoko Muraoka & Hiroyuki Watanabe & Akiko Kanaya & Nobuaki Egashira & Ichiro Ieiri, 2021. "Detection of overdose and underdose prescriptions—An unsupervised machine learning approach," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-14, November.
- David Cemernek & Sandra Cemernek & Heimo Gursch & Ashwini Pandeshwar & Thomas Leitner & Matthias Berger & Gerald Klösch & Roman Kern, 2022. "Machine learning in continuous casting of steel: a state-of-the-art survey," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1561-1579, August.
- Schade, Philipp & Schuhmacher, Monika C., 2023. "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, vol. 19(C).
- Wang, Delu & Tong, Xian & Wang, Yadong, 2020. "An early risk warning system for Outward Foreign Direct Investment in Mineral Resource-based enterprises using multi-classifiers fusion," Resources Policy, Elsevier, vol. 66(C).
- Shuo Xu & Liyuan Hao & Xin An & Dongsheng Zhai & Hongshen Pang, 2019. "Types of DOI errors of cited references in Web of Science with a cleaning method," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1427-1437, September.
- Milan Miric & Hakan Ozalp & Erdem Dogukan Yilmaz, 2023. "Trade‐offs to using standardized tools: Innovation enablers or creativity constraints?," Strategic Management Journal, Wiley Blackwell, vol. 44(4), pages 909-942, April.
- Parminder Singh & Sujatha Krishnamoorthy & Anand Nayyar & Ashish Kr Luhach & Avinash Kaur, 2019. "Soft-computing-based false alarm reduction for hierarchical data of intrusion detection system," International Journal of Distributed Sensor Networks, , vol. 15(10), pages 15501477198, October.
- Erkuş, Ekin Can & Purutçuoğlu, Vilda, 2021. "Outlier detection and quasi-periodicity optimization algorithm: Frequency domain based outlier detection (FOD)," European Journal of Operational Research, Elsevier, vol. 291(2), pages 560-574.
- Gnekpe, Christian & Tchuente, Dieudonné & Nyawa, Serge & Dey, Prasanta Kumar, 2024. "Energy Performance of Building Refurbishments: Predictive and Prescriptive AI-based Machine Learning Approaches," Journal of Business Research, Elsevier, vol. 183(C).
- Kouadri, Abdelmalek & Hajji, Mansour & Harkat, Mohamed-Faouzi & Abodayeh, Kamaleldin & Mansouri, Majdi & Nounou, Hazem & Nounou, Mohamed, 2020. "Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 150(C), pages 598-606.
- López-Díaz, María Concepción & López-Díaz, Miguel & Martínez-Fernández, Sergio, 2023. "On the optimal binary classifier with an application," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
- Manuel Casal-Guisande & Jorge Cerqueiro-Pequeño & José-Benito Bouza-Rodríguez & Alberto Comesaña-Campos, 2023. "Integration of the Wang & Mendel Algorithm into the Application of Fuzzy Expert Systems to Intelligent Clinical Decision Support Systems," Mathematics, MDPI, vol. 11(11), pages 1-33, May.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0218930. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.