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Its main benefit, compared to the typical distributions dedicated to proportional data such as Dirichlet and Beta-Liouville, is its adaptability and explanatory power when dealing with this kind of data. Our goal is to exploit this appropriateness for modeling proportional data to achieve great performance in the anomaly detection task. First, we develop generative models, namely finite mixture models of Libby-Novick Beta-Liouville distributions. Then, we propose two discriminative techniques: Normality scores based on selecting the given distribution to approximate the softmax output vector of a deep classifier and an improved version of Support Vector Machine (SVM) by suggesting a feature mapping approach. We demonstrate the benefits of the presented approaches through a variety of experiments on both image and non-image datasets. The results demonstrate that the proposed anomaly detectors based on the Libby-Novick Beta-Liouville distribution outperform the classical distributions as well as the baseline techniques.<\/jats:p>","DOI":"10.1145\/3675405","type":"journal-article","created":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T15:51:45Z","timestamp":1719676305000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Libby-Novick Beta-Liouville Distribution for Enhanced Anomaly Detection in Proportional Data"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-0077-5300","authenticated-orcid":false,"given":"Oussama","family":"Sghaier","sequence":"first","affiliation":[{"name":"Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5610-8833","authenticated-orcid":false,"given":"Manar","family":"Amayri","sequence":"additional","affiliation":[{"name":"Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7224-7940","authenticated-orcid":false,"given":"Nizar","family":"Bouguila","sequence":"additional","affiliation":[{"name":"Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,11,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/1541880.1541882"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3207836"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.applanim.2022.105708"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cosrev.2020.100285"},{"key":"e_1_3_2_6_2","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.asoc.2017.06.035","article-title":"Multivariate time series anomaly detection: A framework of Hidden Markov Models","volume":"60","author":"Li J.","year":"2017","unstructured":"J. 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Huang (Eds.), Springer, 127\u2013156."},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2020.3039798"},{"key":"e_1_3_2_47_2","doi-asserted-by":"crossref","first-page":"3195","DOI":"10.1007\/s00500-020-05373-x","article-title":"Improving security using SVM-based anomaly detection: issues and challenges","volume":"25","author":"Hosseinzadeh A.","year":"2021","unstructured":"A. Hosseinzadeh, A. M. Rahmani, B. Vo, M. Bidaki, M. Masdari and M. Zangakani. 2021. Improving security using SVM-based anomaly detection: issues and challenges. 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John Wiley and Sons."},{"key":"e_1_3_2_55_2","unstructured":"S. Zagoruyko and N. Komodakis. 2016. Wide residual networks. arXiv:1605.07146. Retrieved from https:\/\/arxiv.org\/abs\/1605.07146"},{"key":"e_1_3_2_56_2","first-page":"45","article-title":"Support vector machine active learning with applications to text classification","volume":"2","author":"Tong S.","year":"2001","unstructured":"S. Tong and D. Koller. 2001. Support vector machine active learning with applications to text classification. Journal of Machine Learning Research 2 (2001), 45\u201366.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2007.07.001"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994018"},{"key":"e_1_3_2_59_2","unstructured":"A. Krizhevsky and Hinton. 2010. Convolutional Deep Belief Networks on CIFAR-10. Unpublished manuscript."},{"key":"e_1_3_2_60_2","unstructured":"H. Xiao K. Rasul and R. Vollgraf. 2017. Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms. arXiv:1708.07747. Retrieved from https:\/\/arxiv.org\/abs\/1708.07747"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.3390\/app9153169"},{"key":"e_1_3_2_62_2","doi-asserted-by":"crossref","unstructured":"D. Protic. 2018. Review of KDD Cup \u201999 NSL-KDD and Kyoto 2006+ datasets. Vojnotehnicki glasnik\/Military Technical Courier 66 3 (2018) 580\u2013596.","DOI":"10.5937\/vojtehg66-16670"},{"key":"e_1_3_2_63_2","unstructured":"https:\/\/www.kaggle.com\/datasets\/sahistapatel96\/bankadditionalfullcsv"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-10925-7_1"},{"key":"e_1_3_2_65_2","first-page":"1100","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Zhai S.","year":"2016","unstructured":"S. Zhai, Y. Cheng, W. Lu, and Z. Zhang. 2016. Deep structured energy based models for anomaly detection. 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