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Hybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture

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dc.contributor.authorMukhtar
dc.date.accessioned2024-03-27T01:29:31Z
dc.date.available2024-03-27T01:29:31Z
dc.date.issued2023-01
dc.description.abstractIn this research, 29 independent single variables and 435 independent interaction variables were identified. The limitation of this research were to address the problems such as irrelevant variables, multicollinearity and outliers.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/18776
dc.subjectHybrid Model In Machine Learning With Robust Regression
dc.subjectHandling Multicollinearity Outlier In Big Data
dc.subjectApplication To Agriculture
dc.titleHybrid Model In Machine Learning With Robust Regression For Handling Multicollinearity Outlier In Big Data And Its Application To Agriculture
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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