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VisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimization
Linnaeus University, Sweden.ORCID iD: 0000-0002-9079-2376
Linnaeus University, Sweden.ORCID iD: 0000-0002-2901-935X
Linnaeus University, Sweden.ORCID iD: 0000-0002-1907-7820
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, Faculty of Science & Engineering. Linnaeus University, Sweden. (iVis, INV)ORCID iD: 0000-0002-0519-2537
2021 (English)In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 40, no 3, p. 201-214Article in journal (Refereed) Published
Abstract [en]

During the training phase of machine learning (ML) models, it is usually necessary to configure several hyperparameters. This process is computationally intensive and requires an extensive search to infer the best hyperparameter set for the given problem. The challenge is exacerbated by the fact that most ML models are complex internally, and training involves trial-and-error processes that could remarkably affect the predictive result. Moreover, each hyperparameter of an ML algorithm is potentially intertwined with the others, and changing it might result in unforeseeable impacts on the remaining hyperparameters. Evolutionary optimization is a promising method to try and address those issues. According to this method, performant models are stored, while the remainder are improved through crossover and mutation processes inspired by genetic algorithms. We present VisEvol, a visual analytics tool that supports interactive exploration of hyperparameters and intervention in this evolutionary procedure. In summary, our proposed tool helps the user to generate new models through evolution and eventually explore powerful hyperparameter combinations in diverse regions of the extensive hyperparameter space. The outcome is a voting ensemble (with equal rights) that boosts the final predictive performance. The utility and applicability of VisEvol are demonstrated with two use cases and interviews with ML experts who evaluated the effectiveness of the tool.

Place, publisher, year, edition, pages
John Wiley & Sons , 2021. Vol. 40, no 3, p. 201-214
Keywords [en]
visualization, visual analytics, interpretable machine learning, explainable machine learning, hyperparameter search, evolutionary optimization
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-176506DOI: 10.1111/cgf.14300ISI: 000667924000017OAI: oai:DiVA.org:liu-176506DiVA, id: diva2:1565852
Conference
23rd EG/VGTC Conference on Visualization (EuroVis '21), 14-18 June 2021, Zürich, Switzerland
Funder
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsAvailable from: 2021-06-14 Created: 2021-06-14 Last updated: 2022-11-22Bibliographically approved

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Chatzimparmpas, AngelosMartins, Rafael MessiasKucher, KostiantynKerren, Andreas

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Chatzimparmpas, AngelosMartins, Rafael MessiasKucher, KostiantynKerren, Andreas
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