Citation

If you use Case-Explainer in your research, please cite:

BibTeX

@software{case_explainer2025,
  author = {Whitten, Paul and Wolff, Francis and Papachristou, Chris},
  title = {Case-Explainer: General-Purpose Case-Based Explainability},
  year = {2025},
  url = {https://github.com/paulwhitten/case-explainer}
}

Related Publications

The case-based explainability approach implemented in this package was validated in:

Hardware Trojan Detection

• Whitten, P., Wolff, F., & Papachristou, C. (2024). “Explainable Hardware Trojan Detection Using Case-Based Reasoning.” Journal of Electronic Testing: Theory and Applications (JETTA).

This work demonstrated 99.9% correspondence on hardware trojan detection with 56,959 samples, showing the effectiveness of case-based explanations for security-critical applications.

Theoretical Foundation

The general approach is inspired by:

• Caruana, R., Kangarloo, H., Dionisio, J., Sinha, U., & Johnson, D. (1999). “Case-based explanation of non-case-based learning methods.” Proceedings of the AMIA Symposium, 212-215.

Acknowledgments

• Built with scikit-learn, NumPy, and matplotlib

• Inspired by interpretable machine learning research

• Validated across medical, financial, and security domains