Efficient machine learning on edge computing through data compression techniques

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dc.contributor.authorGomez Larrakoetxea, Nerea
dc.contributor.authorEskubi Astobiza, Joseba
dc.contributor.authorPastor López, Iker
dc.contributor.authorSanz Urquijo, Borja
dc.contributor.authorGarcía Barruetabeña, Jon
dc.contributor.authorZubillaga Rego, Agustín José
dc.date.accessioned2025-07-14T08:14:19Z
dc.date.available2025-07-14T08:14:19Z
dc.date.issued2023-03-29
dc.date.updated2025-07-14T08:14:19Z
dc.description.abstractThis paper discusses the increasing amount of data handled by companies and the need to use Big Data and Data Analytics to extract value from this data. However, due to the large amount of data collected, challenges related to the computational capacity of machines often arise when performing this analysis to acquire relevant information for the organization, especially when we are using edge computing. The paper aims to train machine learning models using compressed data, with two compression techniques applied to the original data. The results show that models trained with compressed data achieved similar accuracy to those trained with uncompressed data, and different compression techniques were compared. The research extended a previous study by analyzing the use of autoencoders for compression and reducing both instances and dimensionality of the dataset. The accuracy rate of the models when trained with compressed data instead of original data was maintained.en
dc.identifier.citationLarrakoetxea, N. G., Astobiza, J. E., Lopez, I. P., Urquijo, B. S., Barruetabena, J. G., & Rego, A. Z. (2023). Efficient machine learning on edge computing through data compression techniques. IEEE Access, 11, 31676-31685. https://doi.org/10.1109/ACCESS.2023.3263391
dc.identifier.doi10.1109/ACCESS.2023.3263391
dc.identifier.eissn2169-3536
dc.identifier.urihttps://hdl.handle.net/20.500.14454/3210
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subject.otherAutoencoder
dc.subject.otherBayesian network
dc.subject.otherBig data
dc.subject.otherEdge computing
dc.subject.otherMachine learning
dc.titleEfficient machine learning on edge computing through data compression techniquesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.endPage31685
oaire.citation.startPage31676
oaire.citation.titleIEEE Access
oaire.citation.volume11
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc-nd/4.0/
oaire.versionVoR
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