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Deeply-Learned Generalized Linear Models with Missing Data. | LitMetric

AI Article Synopsis

  • Deep Learning (DL) methods are increasingly used for supervised learning but face challenges with missing data in datasets.
  • The authors introduce a novel DL architecture that can handle both ignorable and non-ignorable missing data during training, specifically addressing missing not at random (MNAR) situations.
  • Their approach is validated through simulations and a case study on the Bank Marketing dataset, showing that it outperforms existing methods in predicting client subscriptions based on incomplete phone survey data.

Article Abstract

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to various supervised learning problems. However, the greater prevalence and complexity of missing data in such datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems. We propose a new architecture, , that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time. We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness. We conclude with a case study of the Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data. Supplementary materials for this article are available online.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11339858PMC
http://dx.doi.org/10.1080/10618600.2023.2276122DOI Listing

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