Zobraziť v EDS

Sustainable Selection of Machine Learning Algorithm for Gender-bias Attenuated Prediction

Uložené v:
Podrobná bibliografia
Názov: Sustainable Selection of Machine Learning Algorithm for Gender-bias Attenuated Prediction
Autori: Orbay, Raik, 1974, Wikner, Evelina, 1987
Zdroj: IEEE Open Journal of Vehicular Technology. 6:348-358
Predmety: Feature engineering, Energy consumption, Battery electric vehicles, Unsupervised/supervised learning, Driving style classification, Machine learning
Popis: Research into novel approaches like Machine Learning (ML) promotes a new set of opportunities for sustainable development of applications through automation. However, there are certain ML tasks which are prone to spurious classification, mainly due to the bias in legacy data. One well-known and highly actual misclassification case concerns gender. As the vast dataset for engineering rules, standards and experiments are based on men, a bias towards women is the subject of research. Accordingly, any bias should be contained before the algorithms are deployed to the service of the sustainable society. There is a substantial amount of data on ML gender-bias in the literature. In these, the majority of the investigated cases are for ML branches like image or sound processing and text recognition. However, utilizing ML for driving style investigations is not an extensively researched area. In this work, a novel application for gender-based classification with bias-attenuation using anonymized driving data will be presented. Using data devoid of biometric and geographic information, the proposed pipeline distinguishes manifested binary genders with 80% accuracy for the drivers in the holdout data set. In addition, a method for sustainable algorithm selection and its extension to embedded applications, is proposed. An investigation into the environmental burden of seven different types of ML algorithms was conducted and the popular neural network algorithm had the highest environmental burden.
Popis súboru: electronic
Prístupová URL adresa: https://research.chalmers.se/publication/543996
https://research.chalmers.se/publication/543996/file/543996_Fulltext.pdf
Databáza: SwePub
FullText Text:
  Availability: 0
CustomLinks:
  – Url: https://research.chalmers.se/publication/543996#
    Name: EDS - SwePub (s4221598)
    Category: fullText
    Text: View record in SwePub
  – Url: https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=EBSCO&SrcAuth=EBSCO&DestApp=WOS&ServiceName=TransferToWoS&DestLinkType=GeneralSearchSummary&Func=Links&author=Orbay%20R
    Name: ISI
    Category: fullText
    Text: Nájsť tento článok vo Web of Science
    Icon: https://imagesrvr.epnet.com/ls/20docs.gif
    MouseOverText: Nájsť tento článok vo Web of Science
Header DbId: edsswe
DbLabel: SwePub
An: edsswe.oai.research.chalmers.se.facbbbb7.82f6.485c.9a95.2473616b2598
RelevancyScore: 1115
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 1114.736328125
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Sustainable Selection of Machine Learning Algorithm for Gender-bias Attenuated Prediction
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Orbay%2C+Raik%22">Orbay, Raik</searchLink>, 1974<br /><searchLink fieldCode="AR" term="%22Wikner%2C+Evelina%22">Wikner, Evelina</searchLink>, 1987
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <i>IEEE Open Journal of Vehicular Technology</i>. 6:348-358
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Feature+engineering%22">Feature engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink><br /><searchLink fieldCode="DE" term="%22Battery+electric+vehicles%22">Battery electric vehicles</searchLink><br /><searchLink fieldCode="DE" term="%22Unsupervised%2Fsupervised+learning%22">Unsupervised/supervised learning</searchLink><br /><searchLink fieldCode="DE" term="%22Driving+style+classification%22">Driving style classification</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink>
– Name: Abstract
  Label: Description
  Group: Ab
  Data: Research into novel approaches like Machine Learning (ML) promotes a new set of opportunities for sustainable development of applications through automation. However, there are certain ML tasks which are prone to spurious classification, mainly due to the bias in legacy data. One well-known and highly actual misclassification case concerns gender. As the vast dataset for engineering rules, standards and experiments are based on men, a bias towards women is the subject of research. Accordingly, any bias should be contained before the algorithms are deployed to the service of the sustainable society. There is a substantial amount of data on ML gender-bias in the literature. In these, the majority of the investigated cases are for ML branches like image or sound processing and text recognition. However, utilizing ML for driving style investigations is not an extensively researched area. In this work, a novel application for gender-based classification with bias-attenuation using anonymized driving data will be presented. Using data devoid of biometric and geographic information, the proposed pipeline distinguishes manifested binary genders with 80% accuracy for the drivers in the holdout data set. In addition, a method for sustainable algorithm selection and its extension to embedded applications, is proposed. An investigation into the environmental burden of seven different types of ML algorithms was conducted and the popular neural network algorithm had the highest environmental burden.
– Name: Format
  Label: File Description
  Group: SrcInfo
  Data: electronic
– Name: URL
  Label: Access URL
  Group: URL
  Data: <link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/543996" linkWindow="_blank">https://research.chalmers.se/publication/543996</link><br /><link linkTarget="URL" linkTerm="https://research.chalmers.se/publication/543996/file/543996_Fulltext.pdf" linkWindow="_blank">https://research.chalmers.se/publication/543996/file/543996_Fulltext.pdf</link>
PLink https://erproxy.cvtisr.sk/sfx/access?url=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsswe&AN=edsswe.oai.research.chalmers.se.facbbbb7.82f6.485c.9a95.2473616b2598
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1109/OJVT.2024.3502921
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 348
    Subjects:
      – SubjectFull: Feature engineering
        Type: general
      – SubjectFull: Energy consumption
        Type: general
      – SubjectFull: Battery electric vehicles
        Type: general
      – SubjectFull: Unsupervised/supervised learning
        Type: general
      – SubjectFull: Driving style classification
        Type: general
      – SubjectFull: Machine learning
        Type: general
    Titles:
      – TitleFull: Sustainable Selection of Machine Learning Algorithm for Gender-bias Attenuated Prediction
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Orbay, Raik
      – PersonEntity:
          Name:
            NameFull: Wikner, Evelina
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 26441330
            – Type: issn-locals
              Value: SWEPUB_FREE
            – Type: issn-locals
              Value: CTH_SWEPUB
          Numbering:
            – Type: volume
              Value: 6
          Titles:
            – TitleFull: IEEE Open Journal of Vehicular Technology
              Type: main
ResultId 1