Constructing Surrogate Models in Machine Learning Using Combinatorial Testing and Active Learning
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.
Authors
- D. Richard Kuhn (ORCID: https://orcid.org/0000-0003-0050-1596)
- Krishna Khadka (ORCID: https://orcid.org/0009-0000-0672-5107)
- Yu Lei (ORCID: https://orcid.org/0000-0002-1069-5980)
- Raghu N. Kacker (ORCID: https://orcid.org/0000-0002-7666-3391)
- Sunny Shree (ORCID: https://orcid.org/0009-0009-8746-0600)
Institutions
- National Institute of Standards and Technology (US)
- The University of Texas at Arlington (US)
Publication Details
- Published
- 2024-10-18
- DOI
- https://doi.org/10.1145/3691620.3695532
- Citations
- 1
- Primary Topic
- Machine Learning and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.35
Funders
- National Institute of Standards and Technology