Deep adaptive anomaly detection using an active learning framework
| dc.contributor.advisor | Bassett, Bruce | |
| dc.contributor.author | Sekyi, Emmanuel | |
| dc.date.accessioned | 2023-04-18T18:47:35Z | |
| dc.date.available | 2023-04-18T18:47:35Z | |
| dc.date.issued | 2022 | |
| dc.date.updated | 2023-04-18T18:47:19Z | |
| dc.description.abstract | Anomaly detection is the process of finding unusual events in a given dataset. Anomaly detection is often performed on datasets with a fixed set of predefined features. As a result of this, if the normal features bear a close resemblance to the anomalous features, most anomaly detection algorithms exhibit poor performance. This work seeks to answer the question, can we deform these features so as to make the anomalies standout and hence improve the anomaly detection outcome? We employ a Deep Learning and an Active Learning framework to learn features for anomaly detection. In Active Learning, an Oracle (usually a domain expert) labels a small amount of data over a series of training rounds. The deep neural network is trained after each round to incorporate the feedback from the Oracle into the model. Results on the MNIST, CIFAR-10 and Galaxy Zoo datasets show that our algorithm, Ahunt, significantly outperforms other anomaly detection algorithms used on a fixed, static, set of features. Ahunt can therefore overcome a poor choice of features that happen to be suboptimal for detecting anomalies in the data, learning more appropriate features. We also explore the role of the loss function and Active Learning query strategy, showing these are important, especially when there is a significant variation in the anomalies. | |
| dc.identifier.apacitation | Sekyi, E. (2022). <i>Deep adaptive anomaly detection using an active learning framework</i>. (). ,Faculty of Science ,Department of Mathematics and Applied Mathematics. Retrieved from http://hdl.handle.net/11427/37767 | en_ZA |
| dc.identifier.chicagocitation | Sekyi, Emmanuel. <i>"Deep adaptive anomaly detection using an active learning framework."</i> ., ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2022. http://hdl.handle.net/11427/37767 | en_ZA |
| dc.identifier.citation | Sekyi, E. 2022. Deep adaptive anomaly detection using an active learning framework. . ,Faculty of Science ,Department of Mathematics and Applied Mathematics. http://hdl.handle.net/11427/37767 | en_ZA |
| dc.identifier.ris | TY - Master Thesis AU - Sekyi, Emmanuel AB - Anomaly detection is the process of finding unusual events in a given dataset. Anomaly detection is often performed on datasets with a fixed set of predefined features. As a result of this, if the normal features bear a close resemblance to the anomalous features, most anomaly detection algorithms exhibit poor performance. This work seeks to answer the question, can we deform these features so as to make the anomalies standout and hence improve the anomaly detection outcome? We employ a Deep Learning and an Active Learning framework to learn features for anomaly detection. In Active Learning, an Oracle (usually a domain expert) labels a small amount of data over a series of training rounds. The deep neural network is trained after each round to incorporate the feedback from the Oracle into the model. Results on the MNIST, CIFAR-10 and Galaxy Zoo datasets show that our algorithm, Ahunt, significantly outperforms other anomaly detection algorithms used on a fixed, static, set of features. Ahunt can therefore overcome a poor choice of features that happen to be suboptimal for detecting anomalies in the data, learning more appropriate features. We also explore the role of the loss function and Active Learning query strategy, showing these are important, especially when there is a significant variation in the anomalies. DA - 2022 DB - OpenUCT DP - University of Cape Town KW - anomaly detection KW - deep learning KW - active learning LK - https://open.uct.ac.za PY - 2022 T1 - Deep adaptive anomaly detection using an active learning framework TI - Deep adaptive anomaly detection using an active learning framework UR - http://hdl.handle.net/11427/37767 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/37767 | |
| dc.identifier.vancouvercitation | Sekyi E. Deep adaptive anomaly detection using an active learning framework. []. ,Faculty of Science ,Department of Mathematics and Applied Mathematics, 2022 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/37767 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Mathematics and Applied Mathematics | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | anomaly detection | |
| dc.subject | deep learning | |
| dc.subject | active learning | |
| dc.title | Deep adaptive anomaly detection using an active learning framework | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |