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Supervised feature compression based on counterfactual analysis

Author

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  • Piccialli, Veronica
  • Romero Morales, Dolores
  • Salvatore, Cecilia

Abstract

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanations to detect the important decision boundaries of a pre-trained black-box model. This information is used to build a supervised discretization of the features in the dataset with a tunable granularity. Using the discretized dataset, an optimal Decision Tree can be trained that resembles the black-box model, but that is more interpretable and compact. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity.

Suggested Citation

  • Piccialli, Veronica & Romero Morales, Dolores & Salvatore, Cecilia, 2024. "Supervised feature compression based on counterfactual analysis," European Journal of Operational Research, Elsevier, vol. 317(2), pages 273-285.
    • Handle: RePEc:eee:ejores:v:317:y:2024:i:2:p:273-285
    DOI: 10.1016/j.ejor.2023.11.019
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    References listed on IDEAS

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    1. Oktay Günlük & Jayant Kalagnanam & Minhan Li & Matt Menickelly & Katya Scheinberg, 2021. "Optimal decision trees for categorical data via integer programming," Journal of Global Optimization, Springer, vol. 81(1), pages 233-260, September.
    2. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    3. Piramuthu, Selwyn, 2004. "Evaluating feature selection methods for learning in data mining applications," European Journal of Operational Research, Elsevier, vol. 156(2), pages 483-494, July.
    4. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    5. Kozodoi, Nikita & Jacob, Johannes & Lessmann, Stefan, 2022. "Fairness in credit scoring: Assessment, implementation and profit implications," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1083-1094.
    6. Nikita Kozodoi & Johannes Jacob & Stefan Lessmann, 2021. "Fairness in Credit Scoring: Assessment, Implementation and Profit Implications," Papers 2103.01907, arXiv.org, revised Jun 2022.
    7. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
    8. Fethi, Meryem Duygun & Pasiouras, Fotios, 2010. "Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey," European Journal of Operational Research, Elsevier, vol. 204(2), pages 189-198, July.
    Full references (including those not matched with items on IDEAS)

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