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dc.contributor.authorYuan, Fuqing
dc.contributor.authorLu, Jinmei
dc.date.accessioned2023-06-05T07:52:45Z
dc.date.available2023-06-05T07:52:45Z
dc.date.issued2019
dc.description.abstractThe machine learning approach is a new hot topic in recent years that are widely used in different sections, including industries, economy, disaster prediction and politics. After decades’ of development, the available machine learning algorithms are numerous and diverse. Traditional methods such as regression, classical statistical methods, are unfortunately laid aside as non-mainstream. This paper tries to compare the classical regression with machine learning algorithm as classifier. Typical machine learning algorithm support vector machine (SVM) is compared with the classical regression. The classical regression is modified to tailor as classifier. Confidence interval and credibility of prediction from regression is developed to evaluate the prediction uncertainty. Benchmark data from public database is used to demonstrate the performance. The results showed that regression exhibits an efficient computational cost with comparative accuracy.en_US
dc.identifier.citationYuan F, Lu J. Against the Trend-An tentative Data Analysis Method using Classical Regression against Machine Learning Approach. 2019 International Conference on Machine Learning and Cybernetics (ICMLC). 2019en_US
dc.identifier.cristinIDFRIDAID 1762808
dc.identifier.issn2160-1348
dc.identifier.urihttps://hdl.handle.net/10037/29343
dc.language.isoengen_US
dc.relation.journal2019 International Conference on Machine Learning and Cybernetics (ICMLC)
dc.rights.accessRightsopenAccessen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleAgainst the Trend-An tentative Data Analysis Method using Classical Regression against Machine Learning Approachen_US
dc.type.versionsubmittedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US


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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)