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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
Linköpings universitet, Institutionen för teknik och naturvetenskap, Medie- och Informationsteknik. Linköpings universitet, Tekniska fakulteten. Linnaeus University, Sweden. (iVis, INV)ORCID-id: 0000-0002-0519-2537
2021 (Engelska)Ingår i: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 40, nr 3, s. 201-214Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
John Wiley & Sons , 2021. Vol. 40, nr 3, s. 201-214
Nyckelord [en]
visualization, visual analytics, interpretable machine learning, explainable machine learning, hyperparameter search, evolutionary optimization
Nationell ämneskategori
Datavetenskap (datalogi)
Identifikatorer
URN: urn:nbn:se:liu:diva-176506DOI: 10.1111/cgf.14300ISI: 000667924000017OAI: oai:DiVA.org:liu-176506DiVA, id: diva2:1565852
Konferens
23rd EG/VGTC Conference on Visualization (EuroVis '21), 14-18 June 2021, Zürich, Switzerland
Forskningsfinansiär
ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsTillgänglig från: 2021-06-14 Skapad: 2021-06-14 Senast uppdaterad: 2022-11-22Bibliografiskt granskad

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Chatzimparmpas, AngelosMartins, Rafael MessiasKucher, KostiantynKerren, Andreas

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Chatzimparmpas, AngelosMartins, Rafael MessiasKucher, KostiantynKerren, Andreas
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