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dc.contributor.authorChatzimparmpas, Angelosen_US
dc.contributor.authorMartins, Rafael M.en_US
dc.contributor.authorKucher, Kostiantynen_US
dc.contributor.authorKerren, Andreasen_US
dc.contributor.editorBorgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana vonen_US
dc.date.accessioned2021-06-12T11:01:36Z
dc.date.available2021-06-12T11:01:36Z
dc.date.issued2021
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14300
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14300
dc.description.abstractDuring 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.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectHuman
dc.subjectcentered computing!Visualization
dc.subjectVisual analytics
dc.subjectMachine learning!Supervised learning
dc.titleVisEvol: Visual Analytics to Support Hyperparameter Search through Evolutionary Optimizationen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersMachine Learning and Explainable AI
dc.description.volume40
dc.description.number3
dc.identifier.doi10.1111/cgf.14300
dc.identifier.pages201-214


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  • 40-Issue 3
    EuroVis 2021 - Conference Proceedings

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