IFGAN uses feature-specific GANs for missing value imputation.
problem Missing value imputation in data mining.
method Feature-specific Generative Adversarial Networks (GAN).
result IFGAN outperforms state-of-the-art algorithms in various missing conditions.
A new imputation method MissARF uses adversarial random forests for fast and accurate missing value imputation.
problem Handling missing values in biostatistical analyses.
method Adversarial Random Forests (ARF) for density estimation and data synthesis.
result MissARF performs comparably to state-of-the-art methods in imputation quality and runtime.
Double autoencoder Ae2I improves missing value imputation in recommender systems.
problem Imputing missing values in tables using row-row and column-column relationships.
method Simultaneously uses row-row and column-column relationships through a double autoencoder.
result Ae2I outperforms state-of-the-art models in recommender systems. kNNSampler imputes missing values from their distributions using kNN.
problem Impute missing values from their distributions.
method Randomly samples from the observed responses of the k most similar units.
result Estimates the conditional distribution of missing values.
RDIS fills missing values in time series data explicitly.
problem Missing values in time series data.
method Random Drop Imputation with Self-training.
result RDIS achieves competitive results on real-world datasets.
Gaussian Processes improve missing value imputation in datasets.
problem Handling missing values in large datasets.
method Sparse Gaussian Processes combined with stochastic variational inference.
result MGP significantly outperforms other imputation methods.
Imputation-free method learns tabular data with missing values using transformer.
problem Machine learning on tabular data with missing values often leads to unreliable outcomes due to synthetic imputation.
method Incremental attention learning (IFIAL) using transformer with attention masks.
result IFIAL outperforms state-of-the-art methods in 17 diverse tabular data sets.
A new imputation method considers feature importance for missing value completion.
problem Missing values in datasets reduce accuracy and increase processing difficulty.
method Iterative matrix completion with feature importance learning.
result The method outperforms existing imputation algorithms on various datasets.
DeepIFSAC uses attention mechanisms and contrastive learning to impute missing values in tabular data.
problem Missing values in tabular data, especially when high and not random.
method Row and column attention in a contrastive learning framework with CutMix data augmentation.
result Proposed method outperforms state-of-the-art methods for missing rates between 10% and 90% and various missing value types.
New algorithms handle missing data to improve fairness in machine learning.
problem Missing values in data can lead to unfair outcomes in machine learning models.
method Developed scalable and adaptive algorithms to handle missing values while preserving predictive information.
result Our adaptive algorithms consistently achieve higher fairness and accuracy than standard impute-then-classify methods.
Researchers improve imputation of missing data using diffusion transformers, quantifying uncertainty.
problem Enhancing the quality of time-series data with missing values.
method Conditional diffusion transformers for imputation, with statistical sample complexity bounds and uncertainty quantification.
result Theoretical insights into the efficiency and accuracy of imputation, influenced by missing patterns.
Imputation with missing-indicators improves machine learning performance, especially for categorical attributes.
problem Missing values in datasets can be lost through imputation, potentially reducing model performance.
method Combines missing-indicator approach with imputation strategies on real datasets.
result Missing-indicators generally increase classification performance, especially for categorical attributes.
New framework predicts time series with missing values without imputation.
problem Predicting time series with missing values, especially when there's no ground truth for missing data.
method CRIB framework, combining attention mechanism and consistency regularization.
result CRIB framework predicts accurately even under high missing rates.
Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing valu…
New method for fMRI missing value imputation improves robustness.
problem High frequency of missing values in fMRI data.
method Spatial and time-dependent regularization with a novel recurrent layer.
result Improved robustness against state-of-the-art alternatives.
VAEs struggle with missing data imputation, especially for extreme values.
problem Imputation of missing data in complex, non-linear relationships.
method Investigated variational autoencoders (VAEs) for multiple imputation and improved with β-VAEs.
result β-VAEs provide better uncertainty calibration and avoid false discoveries.
New research shows imputation and regression together can predict better than separate steps.
problem Predicting with data missing values without strong assumptions.
method Proposes a joint imputation and regression approach using NeuMiss neural network.
result Joint imputation and regression outperforms separate imputation and regression methods.
In many machine learning applications, we are faced with incomplete datasets. In the literature, missing data imputation techniques have been mostly concerned with filling missing values. However, the existence of missing values is synonymous with uncertainties not only over the distribution of missing values but also …
Generative imputation network improves missing value estimation.
problem Missing data in statistical analysis.
method Integrates generative networks with chained equations for robust multiple imputation.
result GCMI outperforms other imputation techniques in simulations and real data.
Optimal transport distances help impute missing data.
problem Missing data in real-world datasets.
method Use optimal transport distances as a loss function to impute missing data values.
result OT-based methods match or outperform state-of-the-art imputation methods.
A new method uses Hamiltonian Monte Carlo for imputation and augmentation of healthcare data.
problem Missing values in clinical studies lead to biased results and loss of statistical power.
method Folded Hamiltonian Monte Carlo (F-HMC) with Bayesian inference to handle high-dimensional, small sample size datasets.
result The method enriches the quality of data in precision, accuracy, recall, F1 score, and propensity metric.
A new decision tree method tackles fairness in datasets with missing values.
problem Fairness concerns in machine learning models trained on data with missing values.
method An integrated approach based on decision trees that incorporates missing values directly and optimizes a fairness-regularized objective function.
result Our method outperforms existing fairness intervention methods applied to imputed datasets.
A new algorithm for missing data imputation with low RMSE and explainability.
problem Missing data in various domains, especially in critical applications requiring low RMSE and explainability.
method DIMV algorithm that uses conditional distribution of features based on fully observed features.
result DIMV provides low RMSE, scalability, and explainability for imputed values.
Time series are widely used as signals in many classification/regression tasks. It is ubiquitous that time series contains many missing values. Given multiple correlated time series data, how to fill in missing values and to predict their class labels? Existing imputation methods often impose strong assumptions of the …
New online imputation method for mixed data improves accuracy and speed.
problem Missing value imputation in online settings for mixed data types.
method Online Gaussian copula model for imputation and change point detection.
result The model improves accuracy and speed, especially on large datasets.
Machine learning portfolios perform well with simple imputation of missing data.
problem Handling missing values in machine learning portfolios constructed from cross-sectional return predictors.
method Simple imputation with cross-sectional means compared to rigorous expectation-maximization methods.
result Simple imputation performs well due to the structure of missing data.
In many application settings, the data have missing entries which make analysis challenging. An abundant literature addresses missing values in an inferential framework: estimating parameters and their variance from incomplete tables. Here, we consider supervised-learning settings: predicting a target when missing valu…
MTSCI uses diffusion models to impute multivariate time series data with consistency.
problem Imputation of missing values in multivariate time series data.
method MTSCI employs a contrastive complementary mask and mixup mechanism to ensure intra-consistency and inter-consistency.
result MTSCI achieves state-of-the-art performance on multivariate time series imputation tasks.
Bayesian method for imputing missing values in tensor data.
problem Missing data in multi-way arrays (tensors) in biomedical studies.
method Flexible Bayesian framework with conjugate priors for CP factorization.
result Accurately captures uncertainty in microbiome profiles at missing timepoints.
MissBGM uses AI and Bayesian modeling for better missing data imputation.
problem Missing data imputation in data science, especially with uncertainty quantification.
method AI-powered Bayesian generative modeling with explicit modeling of missingness mechanisms.
result MissBGM provides principled posterior uncertainty over imputations and superior performance.
Improved time series classification with imputed data using label-guided forest-based methods.
problem Missing data in time series data.
method Label-guided imputation using forest-based proximity measures.
result Imputation leads to higher classification accuracies, even with imputed values differing from true values.
New method for imputing missing data in multi-view data.
problem Large missing data sets in multi-view data.
method Stacked penalized logistic regression (StaPLR) in a dimension-reduced space.
result New method performs competitively with lower computational cost.
Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are…
CoIFNet unifies imputation and forecasting for robust multivariate time series prediction with missing values.
problem Pervasive missing values degrade multivariate time series forecasting accuracy.
method CoIFNet integrates imputation and forecasting through Cross-Timestep Fusion and Cross-Variate Fusion modules.
result CoIFNet achieves 24.40% improvement over state-of-the-art methods at 0.6 point (block) missing rate.
Proposes CBMI for missing data imputation using labels and input.
problem Missing data in practical data science settings.
method CBMI: imputes labels and input simultaneously; IUL: stacks label into input.
result CBMI improves classification accuracy, especially for imbalanced and categorical data.
Paper proposes methods to learn accurate models from incomplete data without imputation.
problem Learning accurate models from datasets with missing values.
method Unified approach for checking data imputation necessity and efficient algorithms.
result Significant reduction in time and effort needed for data imputation.
Study compares imputation methods' effects on IML confidence intervals.
problem Missing data impacts IML interpretation and confidence intervals.
method Compared single vs multiple imputation methods on IML confidence intervals.
result Multiple imputation provides closer coverage to nominal than single imputation.
Implements SSSD for missing value imputation and forecasting in time series data.
problem Missing values in time series data.
method Structured state space models combined with conditional diffusion models.
result SSSD outperforms state-of-the-art methods on various data sets and missingness scenarios.
GRAPE uses graph representation to handle missing data in feature imputation and label prediction.
problem Handling missing data in machine learning tasks.
method GRAPE uses a bipartite graph where observations and features are nodes, and observed feature values are edges. It formulates feature imputation as edge-level prediction and label prediction as node-level prediction, solving these with Graph Neural Networks.
result GRAPE achieves 20% lower mean absolute error for imputation and 10% lower for label prediction compared to state-of-the-art methods.
A new method for imputing missing data using graphical models.
problem Missing data in graphs and its impact on analysis.
method MMG framework based on conditional independence and PAI principle.
result Valid and efficient method for imputing missing data.
A new method uses deep Gaussian processes to handle missing values in irregularly sampled healthcare data.
problem Missing values and irregular sampling in healthcare data.
method Deep Gaussian process emulation with stochastic imputation.
result The method outperforms conventional imputation methods in clinical datasets.
A new imputation method estimates missing values by matching observed marginals from masked data.
problem Missing values in data undermine statistical and machine learning analysis.
method Estimates a distribution from masked observations using positive semi-definite kernel density estimation.
result The method yields both single and multiple imputations from the same fitted density, with statistical consistency and fast adaptive excess risk.
This work proposes a non-iterative strategy for missing value imputations which is guided by similarity between observations, but instead of explicitly determining distances or nearest neighbors, it assigns observations to overlapping buckets through recursive semi-random hyperplane cuts, in which weighted averages are…
A new model predicts in-hospital mortality from EHR data, handling missing values with uncertainty.
problem Handling missing values in non-stationary, noisy EHR data for accurate mortality prediction.
method Uncertainty-Gated Stochastic Sequential Model using variational recurrent networks.
result The model outperforms state-of-the-art methods in predicting in-hospital mortality.
Missing values widely exist in many real-world datasets, which hinders the performing of advanced data analytics. Properly filling these missing values is crucial but challenging, especially when the missing rate is high. Many approaches have been proposed for missing value imputation (MVI), but they are mostly heurist…
This paper proposes a probabilistic imputation method with uncertainty quantification.
problem Missing value imputation with uncertainty estimation for large datasets.
method Low Rank Gaussian Copula framework that augments PPCA with column-specific transformations.
result The method yields state-of-the-art imputation accuracy and well-calibrated uncertainty estimates.
IRTCI uses IRT for categorical imputation, outperforming other methods in various datasets.
problem Missing data in datasets limits model testing and statistical inference.
method Item Response Theory for Categorical Imputation (IRTCI) based on IRT.
result IRTCI outperformed kNN, MICE, and Datawig in various datasets.
The study proposes algorithms to minimize rating discordance in missing data.
problem Missing ratings in combined rating lists.
method Optimization models and algorithms that minimize total rating discordance.
result The proposed methods outperform state-of-the-art imputation methods in accuracy.