Available at: https://digitalcommons.calpoly.edu/theses/3029
Date of Award
6-2025
Degree Name
MS in Statistics
Department/Program
Statistics
College
College of Science and Mathematics
Advisor
Kelly Bodwin
Advisor Department
Statistics
Advisor College
College of Science and Mathematics
Abstract
The outcome of a search and rescue (SAR) operation is influenced by a complex, non-linear interplay among numerous factors, including geographic context, subject-specific characteristics, and environmental conditions. The high dimensionality and intricate dependencies among these variables pose significant challenges to traditional exploratory modeling approaches, limiting their ability to uncover meaningful patterns and relationships associated with mission success. This study introduces Rules Based Explanations for Generated neighborhoods Around Localized cases (REGAL), a novel adaptation of the Local Interpretable Model-agnostic Explanations (LIME) framework to explain deep multimodal neural networks and what key features it assesses to determine search and rescue success. REGAL addresses limitations of traditional LIME by incorporating decision tree-based explanation mechanisms. These include split-based feature importance and the analysis of feature interactions, offering more contextually relevant and actionable insights into model behavior. REGAL also utilizes a generative modeling approach to create perturbations, to allow for more controlled, yet extensive analysis around a localized case.
Included in
Artificial Intelligence and Robotics Commons, Data Science Commons, Statistical Methodology Commons, Statistical Models Commons