Matches in SemOpenAlex for { <https://semopenalex.org/work/W4367671787> ?p ?o ?g. }
- W4367671787 abstract "Most existing deep ensemble credit scoring models have considered deep neural networks, for which the structures are difficult to design and the modeling results are difficult to interpret. Moreover, the methods of dealing with the class-imbalance problem in these studies are still based on traditional resampling methods. To fill these gaps, we combine a new over-sampling method, the variational autoencoder (VAE), and a deep ensemble classifier, the deep forest (DF), and propose a novel deep ensemble model for credit scoring in internet finance, VAE–DF. We train and test our model using a number of credit scoring datasets in internet finance and find that our model exhibits good performance and can realize a self-adapting depth. The results show that VAE–DF is an effective credit scoring tool, especially for highly class-imbalanced and non-linear datasets in internet finance, due to its strong ability to learn the complex distributions of these datasets." @default.
- W4367671787 created "2023-05-03" @default.
- W4367671787 creator A5022183918 @default.
- W4367671787 creator A5038375177 @default.
- W4367671787 creator A5046645047 @default.
- W4367671787 creator A5068860197 @default.
- W4367671787 creator A5073631276 @default.
- W4367671787 creator A5076366373 @default.
- W4367671787 creator A5078558986 @default.
- W4367671787 date "2023-05-01" @default.
- W4367671787 modified "2023-09-26" @default.
- W4367671787 title "A novel deep ensemble model for imbalanced credit scoring in internet finance" @default.
- W4367671787 cites W1551381317 @default.
- W4367671787 cites W1980770954 @default.
- W4367671787 cites W1989137294 @default.
- W4367671787 cites W1998736688 @default.
- W4367671787 cites W2000950277 @default.
- W4367671787 cites W2035244987 @default.
- W4367671787 cites W2043865575 @default.
- W4367671787 cites W2060591526 @default.
- W4367671787 cites W2075159929 @default.
- W4367671787 cites W2102636708 @default.
- W4367671787 cites W2131816657 @default.
- W4367671787 cites W2143426320 @default.
- W4367671787 cites W2148143831 @default.
- W4367671787 cites W2253923269 @default.
- W4367671787 cites W2620649443 @default.
- W4367671787 cites W2626395746 @default.
- W4367671787 cites W2756182389 @default.
- W4367671787 cites W2761075141 @default.
- W4367671787 cites W2779931100 @default.
- W4367671787 cites W2791132707 @default.
- W4367671787 cites W2895960267 @default.
- W4367671787 cites W2904485001 @default.
- W4367671787 cites W2911964244 @default.
- W4367671787 cites W2920860638 @default.
- W4367671787 cites W2968885519 @default.
- W4367671787 cites W2980493715 @default.
- W4367671787 cites W3008362721 @default.
- W4367671787 cites W3035460133 @default.
- W4367671787 cites W3095606640 @default.
- W4367671787 cites W3120644841 @default.
- W4367671787 cites W3142118701 @default.
- W4367671787 cites W3163283403 @default.
- W4367671787 cites W4223974317 @default.
- W4367671787 cites W4232714830 @default.
- W4367671787 cites W4241727697 @default.
- W4367671787 doi "https://doi.org/10.1016/j.ijforecast.2023.03.004" @default.
- W4367671787 hasPublicationYear "2023" @default.
- W4367671787 type Work @default.
- W4367671787 citedByCount "0" @default.
- W4367671787 crossrefType "journal-article" @default.
- W4367671787 hasAuthorship W4367671787A5022183918 @default.
- W4367671787 hasAuthorship W4367671787A5038375177 @default.
- W4367671787 hasAuthorship W4367671787A5046645047 @default.
- W4367671787 hasAuthorship W4367671787A5068860197 @default.
- W4367671787 hasAuthorship W4367671787A5073631276 @default.
- W4367671787 hasAuthorship W4367671787A5076366373 @default.
- W4367671787 hasAuthorship W4367671787A5078558986 @default.
- W4367671787 hasConcept C101738243 @default.
- W4367671787 hasConcept C108583219 @default.
- W4367671787 hasConcept C110875604 @default.
- W4367671787 hasConcept C119857082 @default.
- W4367671787 hasConcept C119898033 @default.
- W4367671787 hasConcept C124101348 @default.
- W4367671787 hasConcept C136764020 @default.
- W4367671787 hasConcept C150921843 @default.
- W4367671787 hasConcept C154945302 @default.
- W4367671787 hasConcept C2777212361 @default.
- W4367671787 hasConcept C41008148 @default.
- W4367671787 hasConcept C50644808 @default.
- W4367671787 hasConcept C95623464 @default.
- W4367671787 hasConceptScore W4367671787C101738243 @default.
- W4367671787 hasConceptScore W4367671787C108583219 @default.
- W4367671787 hasConceptScore W4367671787C110875604 @default.
- W4367671787 hasConceptScore W4367671787C119857082 @default.
- W4367671787 hasConceptScore W4367671787C119898033 @default.
- W4367671787 hasConceptScore W4367671787C124101348 @default.
- W4367671787 hasConceptScore W4367671787C136764020 @default.
- W4367671787 hasConceptScore W4367671787C150921843 @default.
- W4367671787 hasConceptScore W4367671787C154945302 @default.
- W4367671787 hasConceptScore W4367671787C2777212361 @default.
- W4367671787 hasConceptScore W4367671787C41008148 @default.
- W4367671787 hasConceptScore W4367671787C50644808 @default.
- W4367671787 hasConceptScore W4367671787C95623464 @default.
- W4367671787 hasLocation W43676717871 @default.
- W4367671787 hasOpenAccess W4367671787 @default.
- W4367671787 hasPrimaryLocation W43676717871 @default.
- W4367671787 hasRelatedWork W2674501427 @default.
- W4367671787 hasRelatedWork W2922457425 @default.
- W4367671787 hasRelatedWork W3044458868 @default.
- W4367671787 hasRelatedWork W3136979370 @default.
- W4367671787 hasRelatedWork W3158264953 @default.
- W4367671787 hasRelatedWork W4213225422 @default.
- W4367671787 hasRelatedWork W4250304930 @default.
- W4367671787 hasRelatedWork W4289656111 @default.
- W4367671787 hasRelatedWork W4310989423 @default.
- W4367671787 hasRelatedWork W4318677156 @default.
- W4367671787 isParatext "false" @default.
- W4367671787 isRetracted "false" @default.