Interpretable machine learning models for predicting with missing values
In this thesis, we focus on predictions in the presence of incomplete data at test time, using interpretable models that allow humans to understand the predictions. Interpretability is especially necessary when important decisions are at stake, such as in healthcare.
First, we investigate, the situation where variables are missing in recurrent patterns and sample sizes are small per pattern. We propose SPSM that allows coefficient sharing between a main model and pattern submodels in order to make efficient use of data and to be independent on imputation. To enable interpretability, the model can be expressed as a short description introduced by sparsity.
Then, we explore situations where missingness does not occur in patterns and suggest the sparse linear rule model MINTY that naturally trades off between interpretability and the goodness of fit while being sensitive to missing values at test time. To this end, we learn replacement variables, indicating which features in a rule can be alternatively used when the original feature was not measured, assuming some redundancy in the covariates.
Our results have shown that the proposed interpretable models can be used for prediction with missing values, without depending on imputation. We conclude that more work can be done in evaluating interpretable machine learning models in the context of missing values at test time.
interpretable machine learning
Chalmers, Data- och informationsteknik, Data Science och AI
Lena Stempfle, Fredrik D. Johansson - Learning replacement variables in interpretable rule-based models
Sharing pattern submodels for prediction with missing values
Proceedings of the 37th AAAI Conference on Artificial Intelligence,; (2023)
Paper i proceeding
Predicting progression and cognitive decline in amyloid-positive patients with Alzheimer’s disease
Alzheimers Research and Therapy,; Vol. 13(2021)
Artikel i vetenskaplig tidskrift
WASP AI/MLX Forskarassistent
Wallenberg AI, Autonomous Systems and Software Program, 2019-08-01 -- 2023-08-01.
Data- och informationsvetenskap
C3SE (Chalmers Centre for Computational Science and Engineering)
Analysen, EDIT Building, Hörsalsvägen 11, Chalmers
Opponent: Gaël Varoquaux, Ph.D, Research Director, INRIA (French Computer Science National research), France