Year: 2023
Type: 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, dlglm, 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.
Institution: University of North Carolina at Chapel Hill
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: Using Smartphone Assessments for Personalized Prediction of Problematic Alcohol Use, G2695944788, AIM-AHEAD Coordinating Center - All Four Cores, RI: Small: From a Machine Detector to a Machine Detective: Decisions and Queries with Uncertain and Incomplete Information, Extrapolative Analyses for Reliable Machine Learning Driven Scientific Discovery, G9000064959, G9000079270, G9000117861, G9000292904, Biostatistics for Research in Genomics and Cancer, Selective Targeting of Pancreatic Cancer SPORE, SPORE in Breast Cancer, Integrating tumor and stroma to understand and predict treatment response
OpenAlex ID: W4286231647