Towards Better Modeling with Missing Data: A Contrastive Learning-based
  Visual Analytics Perspective
Laixin Xie Yang Ouyang Longfei Chen Ziming Wu Quan Li
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 enhance
ML performance. These approaches rely on the observed data to estimate the
missing values and therefore encounter three main shortcomings in imputation,
including the need for different imputation methods for various missing data
mechanisms, heavy dependence on the assumption of data distribution, and
potential introduction of bias. This study proposes a Contrastive Learning (CL)
framework to model observed data with missing values, where the ML model learns
the similarity between an incomplete sample and its complete counterpart and
the dissimilarity between other samples. Our proposed approach demonstrates the
advantages of CL without requiring any imputation. To enhance interpretability,
we introduce CIVis, a visual analytics system that incorporates interpretable
techniques to visualize the learning process and diagnose the model status.
Users can leverage their domain knowledge through interactive sampling to
identify negative and positive pairs in CL. The output of CIVis is an optimized
model that takes specified features and predicts downstream tasks. We provide
two usage scenarios in regression and classification tasks and conduct
quantitative experiments, expert interviews, and a qualitative user study to
demonstrate the effectiveness of our approach. In short, this study offers a
valuable contribution to addressing the challenges associated with ML modeling
in the presence of missing data by providing a practical solution that achieves
high predictive accuracy and model interpretability.