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Conference Paper: Ensemble-based Ultrahigh-dimensional Variable Screening

TitleEnsemble-based Ultrahigh-dimensional Variable Screening
Authors
Issue Date2019
PublisherInternational Joint Conference on Artificial Intelligence. The Journal's web site is located at https://www.ijcai.org/past_proceedings
Citation
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019), Macau, China, 10-16 August 2019, p. 3613-3619 How to Cite?
AbstractSince the sure independence screening (SIS) method by Fan and Lv, many different variable screening methods have been proposed based on different measures under different models. However, most of these methods are designed for specific models. In practice, we often have very little information about the data generating process and different methods can result in very different sets of features. The heterogeneity presented here motivates us to combine various screening methods simultaneously. In this paper, we introduce a general ensemble-based framework to efficiently combine results from multiple variable screening methods. The consistency and sure screening property of proposed framework has been established. Extensive simulation studies confirm our intuition that the proposed ensemble-based method is more robust against model specification than using single variable screening method. The proposed ensemble-based method is used to predict attention deficit hyperactivity disorder (ADHD) status using brain function connectivity (FC).
Persistent Identifierhttp://hdl.handle.net/10722/277867
ISSN

 

DC FieldValueLanguage
dc.contributor.authorTu, W-
dc.contributor.authorYang, D-
dc.contributor.authorKong, L-
dc.contributor.authorChe, M-
dc.contributor.authorShi, Q-
dc.contributor.authorLi, G-
dc.contributor.authorTian, G-
dc.date.accessioned2019-10-04T08:02:56Z-
dc.date.available2019-10-04T08:02:56Z-
dc.date.issued2019-
dc.identifier.citationProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI 2019), Macau, China, 10-16 August 2019, p. 3613-3619-
dc.identifier.issn1045-0823-
dc.identifier.urihttp://hdl.handle.net/10722/277867-
dc.description.abstractSince the sure independence screening (SIS) method by Fan and Lv, many different variable screening methods have been proposed based on different measures under different models. However, most of these methods are designed for specific models. In practice, we often have very little information about the data generating process and different methods can result in very different sets of features. The heterogeneity presented here motivates us to combine various screening methods simultaneously. In this paper, we introduce a general ensemble-based framework to efficiently combine results from multiple variable screening methods. The consistency and sure screening property of proposed framework has been established. Extensive simulation studies confirm our intuition that the proposed ensemble-based method is more robust against model specification than using single variable screening method. The proposed ensemble-based method is used to predict attention deficit hyperactivity disorder (ADHD) status using brain function connectivity (FC).-
dc.languageeng-
dc.publisherInternational Joint Conference on Artificial Intelligence. The Journal's web site is located at https://www.ijcai.org/past_proceedings-
dc.relation.ispartofInternational Joint Conference on Artificial Intelligence. Proceedings-
dc.titleEnsemble-based Ultrahigh-dimensional Variable Screening-
dc.typeConference_Paper-
dc.identifier.emailLi, G: gdli@hku.hk-
dc.identifier.authorityLi, G=rp00738-
dc.identifier.doi10.24963/ijcai.2019/501-
dc.identifier.scopuseid_2-s2.0-85074905513-
dc.identifier.hkuros306600-
dc.identifier.spage3613-
dc.identifier.epage3619-
dc.publisher.placeUnited States-

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