When Machine Learning Meets Social Science: A Comparative Study of Ordinary Least Square, Stochastic Gradient Descent, and Support Vector Regression for Exploring the Determinants of Behavioral Intentions to Tuberculosis Screening
DC Field | Value | Language |
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dc.contributor.author | 장다연 | - |
dc.contributor.author | 이병관 | - |
dc.date.accessioned | 2023-07-27T12:08:25Z | - |
dc.date.available | 2023-07-27T12:08:25Z | - |
dc.date.created | 2023-06-09 | - |
dc.date.issued | 2022-12 | - |
dc.identifier.issn | 1738-2084 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/188296 | - |
dc.description.abstract | Regression analysis is one of the most widely utilized methods because of its adaptability and simplicity. Recently, the machine learning (ML) approach, which is one aspect of regression methods, has been gaining attention from researchers, including social science, but there are only a few studies that compared the traditional approaches with the ML approach. This study was conducted to explore the usefulness of the ML approach by comparing the ordinary least square estimate (OLS), the stochastic gradient descent algorithm (SGD), and the support vector regression (SVR) with a model predicting and explaining the tuberculosis screening intention. The optimized models were evaluated by four aspects: computational speed, effect and importance of individual predictor, and model performance. The result demonstrated that each model yielded a similar direction of effect and importance in each predictor, and the SVR with the radial kernel had the finest model performance compared to its computational speed. Finally, this study discussed the usefulness and attentive points of the ML approach when a researcher utilizes it in the field of communication. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | 한국언론학회 | - |
dc.title | When Machine Learning Meets Social Science: A Comparative Study of Ordinary Least Square, Stochastic Gradient Descent, and Support Vector Regression for Exploring the Determinants of Behavioral Intentions to Tuberculosis Screening | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | 이병관 | - |
dc.identifier.doi | 10.20879/acr.2022.19.3.101 | - |
dc.identifier.bibliographicCitation | Asian Communication Research, v.19, no.3, pp.101 - 118 | - |
dc.relation.isPartOf | Asian Communication Research | - |
dc.citation.title | Asian Communication Research | - |
dc.citation.volume | 19 | - |
dc.citation.number | 3 | - |
dc.citation.startPage | 101 | - |
dc.citation.endPage | 118 | - |
dc.type.rims | ART | - |
dc.identifier.kciid | ART002909770 | - |
dc.description.journalClass | 2 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | kci | - |
dc.subject.keywordAuthor | ordinary least square | - |
dc.subject.keywordAuthor | stochastic gradient descent | - |
dc.subject.keywordAuthor | support vector regression | - |
dc.subject.keywordAuthor | determinants of tuberculosis screening intention | - |
dc.identifier.url | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE11179586 | - |
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