C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
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...
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| author | Golshid Ranjbaran Diego Reforgiato Recupero Chanchal K. Roy Kevin A. Schneider |
| author_facet | Golshid Ranjbaran Diego Reforgiato Recupero Chanchal K. Roy Kevin A. Schneider |
| author_sort | Golshid Ranjbaran |
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| description | 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. |
| format | Article |
| id | doaj-art-63ebcfbe8ac449ce91563537f0f1378e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-63ebcfbe8ac449ce91563537f0f1378e2025-01-24T13:20:23ZengMDPI AGApplied Sciences2076-34172025-01-0115267210.3390/app15020672C-SHAP: A Hybrid Method for Fast and Efficient InterpretabilityGolshid Ranjbaran0Diego Reforgiato Recupero1Chanchal K. Roy2Kevin A. Schneider3Department of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5A2, CanadaDepartment of Mathematics and Computer Science, University of Cagliari, 09124 Cagliari, ItalyDepartment of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5A2, CanadaDepartment of Computer Science, University of Saskatchewan, Saskatoon, SK S7N 5A2, CanadaModel 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.https://www.mdpi.com/2076-3417/15/2/672interpretability in machine learninginterpretabilitySHAPLIMEC-SHAPcomputational efficiency |
| spellingShingle | Golshid Ranjbaran Diego Reforgiato Recupero Chanchal K. Roy Kevin A. Schneider C-SHAP: A Hybrid Method for Fast and Efficient Interpretability Applied Sciences interpretability in machine learning interpretability SHAP LIME C-SHAP computational efficiency |
| title | C-SHAP: A Hybrid Method for Fast and Efficient Interpretability |
| title_full | C-SHAP: A Hybrid Method for Fast and Efficient Interpretability |
| title_fullStr | C-SHAP: A Hybrid Method for Fast and Efficient Interpretability |
| title_full_unstemmed | C-SHAP: A Hybrid Method for Fast and Efficient Interpretability |
| title_short | C-SHAP: A Hybrid Method for Fast and Efficient Interpretability |
| title_sort | c shap a hybrid method for fast and efficient interpretability |
| topic | interpretability in machine learning interpretability SHAP LIME C-SHAP computational efficiency |
| url | https://www.mdpi.com/2076-3417/15/2/672 |
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