Constructing Surrogate Models in Machine Learning Using Combinatorial Testing and Active Learning
Abstract
Machine learning (ML)-based models are often black box, making it challenging to understand and interpret their decision-making processes. Surrogate models are constructed to approximate the behavior of a target model and are an essential tool for analyzing black-box models. The construction of a surrogate model typically includes querying the target model with carefully selected data points and using the responses from the target model to infer information about its structure and parameters.
BibTeX
@inproceedings{Shree-al:ASE24,
author = {Sunny Shree and
Krishna Khadka and
Yu Lei and
Raghu N. Kacker and
D. Richard Kuhn},
title = {Constructing Surrogate Models in Machine Learning Using Combinatorial Testing and Active Learning},
booktitle = {ASE},
pages = {1645--1654},
publisher = {{ACM}},
year = {2024},
}