Selective Learning for Incomplete Medical Data

HAN Zongbo; XI Yuhan; SHENG Peizhuo; LIAO Yun; ZHANG Changqing

doi:10.12263/DZXB.20260303

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Selective Learning for Incomplete Medical Data

PAPERS | 更新时间：2026-07-30

- Selective Learning for Incomplete Medical Data
- ACTA ELECTRONICA SINICA Vol. 54, Issue 4, Pages: 1775-1788(2026)
- 作者机构：
  
  1.北京邮电大学信息与通信工程学院，北京 100876
  2.天津科技大学人工智能学院，天津 300457
  3.天津大学人工智能学院，天津 300354
- 作者简介：
- 基金信息：
  
  National Key Research and Development Program of China(2025YFF0515300);National Natural Science Foundation of China(624B2100)
- DOI：10.12263/DZXB.20260303
  CLC： TP181;
- Received：27 March 2026，
  
  Accepted：08 April 2026，
  
  Online First：20 May 2026，
  
  Published：25 April 2026
- 稿件说明：
移动端阅览
韩宗博, 西雨晗, 盛培卓, 等. 面向医疗缺失数据的选择性学习[J]. 电子学报, 2026, 54(04): 1775-1788.

HAN Zongbo, XI Yuhan, SHENG Peizhuo, et al. Selective Learning for Incomplete Medical Data[J]. Acta Electronica Sinica, 2026, 54(04): 1775-1788.
韩宗博, 西雨晗, 盛培卓, 等. 面向医疗缺失数据的选择性学习[J]. 电子学报, 2026, 54(04): 1775-1788. DOI：10.12263/DZXB.20260303

HAN Zongbo, XI Yuhan, SHENG Peizhuo, et al. Selective Learning for Incomplete Medical Data[J]. Acta Electronica Sinica, 2026, 54(04): 1775-1788. DOI：10.12263/DZXB.20260303

摘要

现实世界中的数据普遍存在缺失，此类数据缺失问题会显著劣化机器学习模型的性能表现。应对数据缺失的代表性方法为数据插补，即基于已观测到的数据对缺失数据进行估计，进而利用插补后的完整数据开展模型训练。然而，若插补过程中存在估计偏差，则可能引入额外噪声，往往会阻碍模型的学习过程。例如，低质量的插补往往带有估计偏差，这会影响数据的真实分布。模型若基于这些有偏样本建立映射关系，将难以捕捉数据的本质规律，进而导致泛化性能显著衰减。为此，本文提出了一种新的处理缺失数据的方法——面向缺失数据的选择性学习（Selective Learning for Incomplete Data，SLID），旨在有效利用缺失数据进行学习。SLID致力于构建一个能够动态感知数据质量的可靠学习框架。该方法在训练过程中引入动态评估机制，将被动接受插补数据转变为主动筛选：对于高置信度的插补样本，模型将其作为有效特征进行充分学习；而对于低置信度的样本，则引入正则化策略将其视为不可靠信息进行平滑处理。通过这种选择性学习机制，有效地规避了对有偏分布的过拟合，从而显著提升了泛化性能。基于多个数据集上的大量实验结果表明，与以往方法相比，SLID在准确性与可靠性方面均取得了显著提升。

Abstract

In real-world applications

data commonly suffer from missing values

which can substantially degrade the performance of machine learning models. A widely adopted approach to handling missing data is data imputation

where missing entries are estimated from observed data and models are subsequently trained on the completed dataset. However

imputation inevitably introduces estimation errors

and inaccurate imputations may inject additional noise that hinders effective learning. In particular

low-quality imputations often exhibit systematic bias

distorting the true underlying data distribution. When models are trained on such biased samples

they struggle to capture the intrinsic data-generating patterns

resulting in a pronounced decline in generalization performance. To address this issue

we propose a novel framework for learning with missing data

termed selective learning for incomplete data (SLID)

which aims to more effectively exploit incomplete data during model training. SLID is designed as a reliable learning paradigm that dynamically accounts for data quality. Specifically

it introduces a dynamic assessment mechanism that transforms the conventional passive reliance on imputed data into an active selection process. Imputed samples with high confidence are treated as reliable and are fully leveraged for learning

whereas low-confidence samples are regarded as unreliable and are accordingly smoothed through regularization strategies. Through this selective learning mechanism

retaining informative components while suppressing misleading ones

SLID effectively avoids overfitting to biased distributions and substantially enhances generalization performance. Extensive experimental results on multiple datasets demonstrate that

compared with existing approaches

SLID achieves significant improvements in both accuracy and reliability.

关键词

Keywords

references

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