Informed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection

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dc.contributor.authorMahdavi, Atefeh
dc.contributor.authorCarvalho, Marco
dc.date.accessioned2023-12-26T18:36:41Z
dc.date.available2023-12-26T18:36:41Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2023.131
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other24f0a598-20c3-41a7-981a-0f66a131b656
dc.identifier.urihttps://hdl.handle.net/10125/106508
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Science and Machine Learning to Support Business Decisions
dc.subjectdecision support systems
dc.subjectdeep learning
dc.subjectmachine learning
dc.subjectopen set recognition
dc.subjectrepresentation learning
dc.titleInformed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractMachine learning-based techniques open up many opportunities and improvements to derive deeper and more practical insights from data that can help businesses make informed decisions. However, the majority of these techniques focus on the conventional closed-set scenario, in which the label spaces for the training and test sets are identical. Open set recognition (OSR) aims to bring classification tasks in a situation that is more like reality, which focuses on classifying the known classes as well as handling unknown classes effectively. In such an open-set problem the gathered samples in the training set cannot encompass all the classes and the system needs to identify unknown samples at test time. On the other hand, building an accurate and comprehensive model in a real dynamic environment presents a number of obstacles, because it is prohibitively expensive to train for every possible example of unknown items, and the model may fail when tested in testbeds. This study provides an algorithm exploring a new representation of feature space to improve classification in OSR tasks. The efficacy and efficiency of business processes and decision-making can be improved by integrating OSR, which offers more precise and insightful predictions of outcomes. We demonstrate the performance of the proposed method on the MNIST dataset. The results indicate that the proposed model outperforms the baseline methods in accuracy and F1-score.
dcterms.extent10 pages
prism.startingpage1090

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