C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
Source abstract
Abstract
Model interpretability is essential in machine learning, particularly for applications in critical fields like healthcare, where understanding model decisions is paramount. While SHAP (SHapley Additive exPlanations) has proven to be a robust tool for explaining machine learning predictions, its high computational cost limits its practicality for real-time use. To address this, we introduce C-SHAP (Clustering-Boosted SHAP), a hybrid method that combines SHAP with K-means clustering to reduce execution times significantly while preserving interpretability. C-SHAP excels across various datasets and machine learning methods, matching SHAP’s accuracy in selected features while maintaining an accuracy of 0.73 for Random Forest with substantially faster performance. Notably, in the Diabetes dataset collected by the National Institute of Diabetes and Digestive and Kidney Diseases, C-SHAP reduces the execution time from nearly 2000 s to just 0.21 s, underscoring its potential for scalable, efficient interpretability in time-sensitive applications. Such advancements in interpretability and efficiency may hold value for enhancing decision-making within software-intensive systems, aligning with evolving engineering approaches.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Explainable Artificial Intelligence (XAI) · Machine Learning and Data Classification · Machine Learning in Healthcare
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5A2, Canada
Department of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, Italy
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