Supervised Feature Compression based on Counterfactual Analysis

作     者：Piccialli, Veronica Morales, Dolores Romero Salvatore, Cecilia 

作者机构：Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Rome Italy Department of Economics Copenhagen Business School Frederiksberg Denmark Department of Civil Engineering and Computer Science University of Rome Tor Vergata Rome Italy 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Decision trees 

摘      要：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 interpretable and compact. Numerical results on real-world datasets show the effectiveness of the approach in terms of accuracy and sparsity. Copyright © 2022, The Authors. All rights reserved.