arXiv research

Study on missing data mechanisms and simple imputation methods in fairness of machine learning algorithms.

Missing data imputation can help improve the performance of prediction models in situations where missing data hide useful information. This paper compares methods for imputing missing categorical data for supervised classification tasks. We experiment on two machine learning benchmark datasets with missing categorical…

Develops a VAE model for datasets with missing data.

Autoencoder neural network is implemented to estimate the missing data. Genetic algorithm is implemented for network optimization and estimating the missing data. Missing data is treated as Missing At Random mechanism by implementing maximum likelihood algorithm. The network performance is determined by calculating the…

New algorithms handle missing data to improve fairness in machine learning.

Trinary decision tree improves handling of missing data in machine learning.

DeepIFSAC uses attention mechanisms and contrastive learning to impute missing values in tabular data.

This paper compares imputation and direct parameter estimation methods for missing data in correlation matrix visualization.

This tutorial covers methods for handling missing data in SP and ML.

IFGAN uses feature-specific GANs for missing value imputation.

Missing data enhances privacy in differential privacy.

The paper explores how missing data problems are related to causal inference.

We present an automatic classification method for astronomical catalogs with missing data. We use Bayesian networks, a probabilistic graphical model, that allows us to perform inference to pre- dict missing values given observed data and dependency relationships between variables. To learn a Bayesian network from incom…

MIM adds indicator variables to improve model performance on incomplete data.

ELMV uses ensemble learning to handle missing values in EHR data.

Robust variable selection for high-dimensional data with missing and measurement errors.

Two methods use BART to model missing data in leaf photosynthetic trait data.

Gaussian Processes improve missing value imputation in datasets.

Sparse regression such as the Lasso has achieved great success in handling high-dimensional data. However, one of the biggest practical problems is that high-dimensional data often contain large amounts of missing values. Convex Conditioned Lasso (CoCoLasso) has been proposed for dealing with high-dimensional data with…

A new method for imputing missing data using graphical models.

Paper explores Bayes rule for Gaussian mixtures with missing data, outperforming supervised classifiers.

Proposes methods to handle missing data in clustering models.

DIVE learns video representations even with missing data.

Deep learning for missing data with flexible modelling.

Two new methods improve clustering with missing data.

BlockEcho method improves imputation of block-wise missing data.

Framework improves PV forecasting by accounting for missing data uncertainty.

GANs improve missing data imputation for ranking tasks.

Paper develops adaptive models for robust energy forecasting with missing data.

Study compares traditional and machine learning methods for handling missing data in longitudinal studies.

Imputation-free method learns tabular data with missing values using transformer.

New adaptive models improve prediction accuracy with missing data.

VAEs struggle with missing data imputation, especially for extreme values.

When sensors collect spatio-temporal data in a large geographical area, the existence of missing data cannot be escaped. Missing data negatively impacts the performance of data analysis and machine learning algorithms. In this paper, we study deep autoencoders for missing data imputation in spatio-temporal problems. We…

Study improves conformal prediction for missing covariate data.

Missing values frequently arise in modern biomedical studies due to various reasons, including missing tests or complex profiling technologies for different omics measurements. Missing values can complicate the application of clustering algorithms, whose goals are to group points based on some similarity criterion. A c…

The paper analyzes $k$-means clustering for missing data, proving statistical guarantees under MCAR.

Method infers dynamics from incomplete time series data.

Although the sequence-to-sequence (encoder-decoder) model is considered the state-of-the-art in deep learning sequence models, there is little research into using this model for recovering missing sensor data. The key challenge is that the missing sensor data problem typically comprises three sequences (a sequence of o…

Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data. Matrix completion based on low-rank assumptions are very powerful solution for dealing with missing values. However, existing methods do not consider the case of informative mi…

A deep generative model improves imputation of MNAR data by treating missing and complete data equally.

This paper addresses clustering with missing data using Rubin's rules.

The causes underlying unfair decision making are complex, being internalised in different ways by decision makers, other actors dealing with data and models, and ultimately by the individuals being affected by these decisions. One frequent manifestation of all these latent causes arises in the form of missing values: p…

Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data is thought of as missing data. This paper addresses parameter estimation for partially ranked data under a (possibly) non-ignorable missing…

Extends model-x framework to handle missing data.

HH-VAEM improves imputation and acquisition of missing data using hierarchical models and Hamiltonian Monte Carlo.

In this paper, we examine the problem of missing data in high-dimensional datasets by taking into consideration the Missing Completely at Random and Missing at Random mechanisms, as well as theArbitrary missing pattern. Additionally, this paper employs a methodology based on Deep Learning and Swarm Intelligence algorit…

A probabilistic query may not be estimable from observed data corrupted by missing values if the data are not missing at random (MAR). It is therefore of theoretical interest and practical importance to determine in principle whether a probabilistic query is estimable from missing data or not when the data are not MAR.…