用集成学习提升生物医学数据缺失值填补效果
Meta-Imputation Balanced (MIB): An Ensemble Approach for Handling Missing Data in Biomedical Machine Learning
- 训练多个基础填补模型，再通过元学习选择最优结果
- 在合成缺失数据上验证，显著优于单一方法
- 适合医疗、生物信息学等缺失数据多的领域
缺失数据是机器学习中的核心挑战，尤其在生物信息学和临床机器学习中，数据常因采集方式导致不完整。尽管已有多种填补方法，从基础统计到深度学习模型，但无一在各类数据集与缺失机制下表现稳定。本文提出一种新型元填补方法——元填补均衡（MIB），通过在已知真实值的合成掩码数据上训练，学习如何组合多个基础填补模型的输出，以预测最合适的缺失值。该方法展示了集成学习在填补任务中的潜力，为实际系统中更鲁棒、模块化且可解释的预处理流程提供了新路径。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。