A robust ensemble feature selector based on rank aggregation for developing new VO2max prediction models using support vector machines

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dc.contributor.authorAbut, Fatih
dc.contributor.authorAkay, Mehmet Fatih
dc.contributor.authorGeorge, James
dc.date.accessioned2025-01-06T17:37:44Z
dc.date.available2025-01-06T17:37:44Z
dc.date.issued2019
dc.description.abstractThis paper proposes a new ensemble feature selector, called the majority voting feature selector (MVFS), for developing new maximal oxygen uptake (VO(2)max) prediction models using a support vector machine (SVM). The approach is based on rank aggregation, which meaningfully utilizes the correlation among the relevance ranks of predictor variables given by three state-of-the-art feature selectors: Relief-F, minimum redundancy maximum relevance (mRMR), and maximum likelihood feature selection (MLFS). By applying the SVM combined with MVFS on a self-created dataset containing maximal and submaximal exercise data from 185 college students, several new hybrid VO(2)max prediction models have been created. To compare the performance of the proposed ensemble approach on prediction of VO(2)max, SVM-based models with individual combinations of Relief-F, mRMR, and MLFS as well as with other alternative ensemble feature selectors from the literature have also been developed. The results reveal that MVFS outperforms other individual and ensemble feature selectors and yields up to 8.76% increment and 11.15% decrement rates in multiple correlation coefficients (Rs) and root mean square errors (RMSEs), respectively. Furthermore, in addition to reconfirming the relevance of sex, age, and maximal heart rate in predicting VO(2)max, which were previously reported in the literature, it is revealed that submaximal heart rates and exercise times at 1.5-mile distance are two further discriminative predictors of VO(2)max. The results have also been compared to those obtained by a general regression neural network and single decision tree combined with MVFS, and it is shown that the SVM exhibits much better performance than other methods for prediction of VO(2)max.
dc.identifier.doi10.3906/elk-1808-138
dc.identifier.endpage3664
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85072612849
dc.identifier.scopusqualityQ2
dc.identifier.startpage3648
dc.identifier.trdizinid337475
dc.identifier.urihttps://doi.org/10.3906/elk-1808-138
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/337475
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2322
dc.identifier.volume27
dc.identifier.wosWOS:000486425400027
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.publisherTubitak Scientific & Technological Research Council Turkey
dc.relation.ispartofTurkish Journal of Electrical Engineering and Computer Sciences
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectEnsemble feature selection
dc.subjectrank aggregation
dc.subjectsupport vector machine
dc.subjectmaximal oxygen uptake
dc.subjectprediction
dc.titleA robust ensemble feature selector based on rank aggregation for developing new VO2max prediction models using support vector machines
dc.typeArticle

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