DeepAI AI Chat
Log In Sign Up

Deeply-Learned Generalized Linear Models with Missing Data

by   David K. Lim, et al.

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in modern biomedical 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 a Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data.


page 18

page 19

page 21


Missing Data Imputation for Supervised Learning

This paper compares methods for imputing missing categorical data for su...

Neumann networks: differential programming for supervised learning with missing values

The presence of missing values makes supervised learning much more chall...

Are labels informative in semi-supervised learning? – Estimating and leveraging the missing-data mechanism

Semi-supervised learning is a powerful technique for leveraging unlabele...

Comparison of Several Sparse Recovery Methods for Low Rank Matrices with Random Samples

In this paper, we will investigate the efficacy of IMAT (Iterative Metho...

Missing Data Estimation in High-Dimensional Datasets: A Swarm Intelligence-Deep Neural Network Approach

In this paper, we examine the problem of missing data in high-dimensiona...

Contextuality from missing and versioned data

Traditionally categorical data analysis (e.g. generalized linear models)...

Learning the Truth From Only One Side of the Story

Learning under one-sided feedback (i.e., where examples arrive in an onl...