Handling Missing Data in Downstream Tasks With Distribution-Preserving Guarantees
Missing feature values are a significant hurdle for downstream machine-learning tasks such as classification. However, imputation methods for classification might be time-consuming for high-dimensional data, and offer few theoretical guarantees on the preservation of the data distribution and imputation quality, especially for not-missing-at-random mechanisms. First, we propose an imputation approach named F3I based on the iterative improvement of a K-nearest neighbor imputation, where neighbor-specific weights are learned through the optimization of a novel concave, differentiable objective function related to the preservation of the data distribution on non-missing values. F3I can then be chained to and jointly trained with any classifier architecture. Second, we provide a theoretical analysis of imputation quality and data distribution preservation by F3I for several types of missing mechanisms. Finally, we demonstrate the superior performance of F3I on several imputation and classification tasks, with applications to drug repurposing and handwritten-digit recognition data.
Code (0)
등록된 구현이 없습니다.
Tasks
Drug DiscoveryHandwritten Digit RecognitionImputationMissing ValuesSimilar Papers 제목 키워드 기반
Handling Incomplete Heterogeneous Data using a Data-Dependent Kernel
Handling incomplete data in real-world applications is a critical challenge due to two key limitations of existing methods: (i) they are primarily designed for numeric data and struggle with categorical or heterogeneous/…
Missing ValuesDBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data
Multivariate time series(MTS) is a universal data type related to many practical applications. However, MTS suffers from missing data problems, which leads to degradation or even collapse of the downstream tasks, such as…
Time SeriesTime Series AnalysisHandling Missing Data with Graph Representation Learning
Machine learning with missing data has been approached in two different ways, including feature imputation where missing feature values are estimated based on observed values, and label prediction where downstream labels…
Graph Representation LearningImputationPredictionRepresentation LearningTowards Better Modeling with Missing Data: A Contrastive Learning-based Visual Analytics Perspective
Missing data can pose a challenge for machine learning (ML) modeling. To address this, current approaches are categorized into feature imputation and label prediction and are primarily focused on handling missing data to…
Contrastive LearningImputationMissing ValuesPI-NAIM: Path-Integrated Neural Adaptive Imputation Model
Medical imaging and multi-modal clinical settings often face the challange of missing modality in their diagnostic pipelines. Existing imputation methods either lack representational capacity or are computationally expen…
Mortality Prediction