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MISNN: Multiple Imputation via Semi-parametric Neural Networks.

Zhiqi Bu1, Zongyu Dai1, Yiliang Zhang1

  • 1Groups of Applied Mathematics and Computational Science, University of Pennsylvania, Philadelphia, USA.

Advances in Knowledge Discovery and Data Mining : ... Pacific-Asia Conference, PAKDD ..., Proceedings. Pacific-Asia Conference on Knowledge Discovery and Data Mining
|February 19, 2024
PubMed
Summary
This summary is machine-generated.

Multiple imputation (MI) with feature selection is improved by MISNN, a novel neural network algorithm. MISNN offers superior accuracy, statistical consistency, and speed for handling missing data in high-dimensional datasets.

Keywords:
ImputationMissing valueSemi-supervised Learning

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Area of Science:

  • Statistics
  • Machine Learning
  • Biomedical Research

Background:

  • Multiple imputation (MI) is crucial for addressing missing data in various research fields to ensure valid downstream analysis.
  • Integrating feature selection into MI models, particularly with penalized regression, is challenging due to computational inefficiency and performance limitations in high-dimensional settings.

Purpose of the Study:

  • To introduce MISNN, a novel and efficient algorithm for multiple imputation that effectively incorporates feature selection.
  • To provide a general and flexible framework for MI with feature selection, compatible with diverse methods and data types.

Main Methods:

  • MISNN leverages neural networks for approximation, enabling seamless integration with any feature selection technique.
  • The framework supports various neural network architectures, accommodates both high- and low-dimensional data, and handles general missing data patterns.

Main Results:

  • Empirical experiments show MISNN significantly outperforms existing state-of-the-art imputation methods, including Bayesian Lasso and matrix completion.
  • MISNN demonstrates superior imputation accuracy, enhanced statistical consistency, and improved computation speed.

Conclusions:

  • MISNN presents a significant advancement in multiple imputation, particularly for high-dimensional data requiring feature selection.
  • The algorithm offers a computationally efficient and high-performing solution for complex missing data problems.