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Saadallah/Morik/2020g: Active Sampling for Learning Interpretable Surrogate Machine Learning Models

Bibtype Inproceedings
Bibkey Saadallah/Morik/2020g
Author Saadallah, Amal and Katharina, Morik
Title Active Sampling for Learning Interpretable Surrogate Machine Learning Models
Journal IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Abstract The use of machine learning methods to inform
consequential decisions is increasingly expanding across many
fields. As a result, the ability to interpret these models has become
to a greater extent crucial to increase the related-technologies
acceptance level and reliability. In this paper, we propose an
active sampling approach for learning accurately interpretable
surrogate machine learning model to better approximate blackbox
models for supervised learning problems. Hence, the surrogate
model is used to learn the black-box model and reflect
its properties. Active sampling is used as an informed sampling
method to adaptively and iteratively build an optimized training
set based on the predictions of the black-box model to enhance
the accuracy of the surrogate model. Subsequently, the surrogate
model is used to interpret and debug the black-box model. The
developed method is flexible and can be used to approximate
any family of black-box models using any type of interpretable
machine learning models, as it only requires the ability to
compute their outputs. It is also applicable to both regression
and classification tasks. In this work, we bring focus to decision
tree due to their proven high interpretability. An experimental
evaluation of the method on several real-world data sets is
presented to show its flexibility and its robustness compared to
traditional approaches for learning surrogate models.
Index Terms—Active Sampling, Interpretable Machine Learning,
Black-box Model, Surrogate Model, Decision Tree
Year 2020
Projekt SFB876-B3
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