Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learning

• URL: http://arxiv.org/abs/2509.03316v1
• Date: Wed, 03 Sep 2025 13:49:54 GMT
• Title: Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learning
• Authors: Fatemeh Azad, Zoran Bosnić, Matjaž Kukar,
• Abstract summary: We propose a novel Meta-Imputation approach that learns to combine the outputs of multiple base imputers to predict missing values more accurately.<n>Our work highlights the potential of ensemble learning in imputation and paves the way for more robust, modular, and interpretable preprocessing pipelines.
• Score: 0.41292255339309664
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Missing data represents a fundamental challenge in machine learning applications, often reducing model performance and reliability. This problem is particularly acute in fields like bioinformatics and clinical machine learning, where datasets are frequently incomplete due to the nature of both data generation and data collection. While numerous imputation methods exist, from simple statistical techniques to advanced deep learning models, no single method consistently performs well across diverse datasets and missingness mechanisms. This paper proposes a novel Meta-Imputation approach that learns to combine the outputs of multiple base imputers to predict missing values more accurately. By training the proposed method called Meta-Imputation Balanced (MIB) on synthetically masked data with known ground truth, the system learns to predict the most suitable imputed value based on the behavior of each method. Our work highlights the potential of ensemble learning in imputation and paves the way for more robust, modular, and interpretable preprocessing pipelines in real-world machine learning systems.

Related papers

• Precision Adaptive Imputation Network : An Unified Technique for Mixed Datasets [0.0]
  This study introduces the Precision Adaptive Imputation Network (PAIN), a novel algorithm designed to enhance data reconstruction.<n>PAIN employs a tri-step process that integrates statistical methods, random forests, and autoencoders, ensuring balanced accuracy and efficiency in imputation.<n>The findings highlight PAIN's superior ability to preserve data distributions and maintain analytical integrity, particularly in complex scenarios where missingness is not completely at random.
  arXiv  Detail & Related papers  (2025-01-18T06:22:27Z)
• MARIA: a Multimodal Transformer Model for Incomplete Healthcare Data [1.02138250640885]
  MARIA is a transformer-based deep learning model designed to address missing data challenges.<n>Unlike conventional approaches that depend on imputation, MARIA utilizes a masked self-attention mechanism.<n>MARIA outperforms existing methods in terms of performance and resilience to varying levels of data incompleteness.
  arXiv  Detail & Related papers  (2024-12-19T13:00:03Z)
• MIBP-Cert: Certified Training against Data Perturbations with Mixed-Integer Bilinear Programs [50.41998220099097]
  Data errors, corruptions, and poisoning attacks during training pose a major threat to the reliability of modern AI systems.<n>We introduce MIBP-Cert, a novel certification method based on mixed-integer bilinear programming (MIBP)<n>By computing the set of parameters reachable through perturbed or manipulated data, we can predict all possible outcomes and guarantee robustness.
  arXiv  Detail & Related papers  (2024-12-13T14:56:39Z)
• In-Database Data Imputation [0.6157028677798809]
  Missing data is a widespread problem in many domains, creating challenges in data analysis and decision making. Traditional techniques for dealing with missing data, such as excluding incomplete records or imputing simple estimates, are computationally efficient but may introduce bias and disrupt variable relationships. Model-based imputation techniques offer a more robust solution that preserves the variability and relationships in the data, but they demand significantly more computation time. This work enables efficient, high-quality, and scalable data imputation within a database system using the widely used MICE method.
  arXiv  Detail & Related papers  (2024-01-07T01:57:41Z)
• HyperImpute: Generalized Iterative Imputation with Automatic Model Selection [77.86861638371926]
  We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models. We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
  arXiv  Detail & Related papers  (2022-06-15T19:10:35Z)
• BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines [49.75878234192369]
  We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model. We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
  arXiv  Detail & Related papers  (2022-02-21T10:34:41Z)
• MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms [82.90843777097606]
  We propose a causally-aware imputation algorithm (MIRACLE) for missing data. MIRACLE iteratively refines the imputation of a baseline by simultaneously modeling the missingness generating mechanism. We conduct extensive experiments on synthetic and a variety of publicly available datasets to show that MIRACLE is able to consistently improve imputation.
  arXiv  Detail & Related papers  (2021-11-04T22:38:18Z)
• Two ways towards combining Sequential Neural Network and Statistical Methods to Improve the Prediction of Time Series [0.34265828682659694]
  We propose two different directions to integrate the two, a decomposition-based method and a method exploiting the statistic extraction of data features. We evaluate the proposal using time series data with varying degrees of stability. Performance results show that both methods can outperform existing schemes that use models and learning separately.
  arXiv  Detail & Related papers  (2021-09-30T20:34:58Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.