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dc.contributor.authorAlaka, Hafiz
dc.contributor.authorOyedele, Lukumon O.
dc.contributor.authorOwolabi, Hakeem O
dc.contributor.authorAkinade, Olugbenga O.
dc.contributor.authorBilal, Muhammad
dc.contributor.authorAjayi, Saheed O.
dc.date.accessioned2019-12-04T01:12:33Z
dc.date.available2019-12-04T01:12:33Z
dc.date.issued2019-11-01
dc.identifier.citationAlaka , H , Oyedele , L O , Owolabi , H O , Akinade , O O , Bilal , M & Ajayi , S O 2019 , ' A Big Data Analytics Approach for Construction Firms Failure Prediction Models ' , IEEE Transactions on Engineering Management , vol. 66 , no. 4 , 8438924 , pp. 689 - 698 . https://doi.org/10.1109/TEM.2018.2856376
dc.identifier.issn0018-9391
dc.identifier.otherPURE: 17736659
dc.identifier.otherPURE UUID: 3c76c039-b56f-41ea-b925-30d0138fc681
dc.identifier.otherBibtex: urn:53811d6199d3709578f2c908a820d7b0
dc.identifier.otherScopus: 85051761533
dc.identifier.urihttp://hdl.handle.net/2299/21955
dc.description© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractUsing 693 000 datacells from 33 000 sample construction firms that operated or failed between 2008 and 2017, failure prediction models were developed using artificial neural network (ANN), support vector machine, multiple discriminant analysis (MDA), and logistic regression (LR). The accuracy of the models on test data surprisingly showed ANN to have only a slightly better accuracy than LR and MDA. The ANN's number of units in the hidden layer and weight decay hyperparameters were consequently tuned using the grid search. Tuning process led to tedious machine computation that was aborted after many hours without completion. The state of art big data analytics (BDA) technology was, for the first time in failure prediction, consequently employed and the tuning was completed in some seconds. Mean accuracy from cross validation was used for selection of the model with best parameter values, which were used to develop a new ANN model that outperformed all previously developed models on test data. Subsequent use of selected variables to develop new models led to reduced tuning computational cost, but not improved performance. Since the real-life effect of a misclassification cost is greater than the tedious computation cost, it was concluded that BDA is the best compromise.en
dc.format.extent10
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Engineering Management
dc.subjectArtificial neural networks
dc.subjectbig data applications
dc.subjectconstruction industry
dc.subjectmachine learning
dc.subjectpredictive models
dc.subjectsupport vector machines
dc.subjectStrategy and Management
dc.subjectElectrical and Electronic Engineering
dc.titleA Big Data Analytics Approach for Construction Firms Failure Prediction Modelsen
dc.contributor.institutionHertfordshire Business School
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85051761533&partnerID=8YFLogxK
rioxxterms.versionAM
rioxxterms.versionofrecordhttps://doi.org/10.1109/TEM.2018.2856376
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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