EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood Generation

• Published in: 2020 International Joint Conference on Neural Networks (IJCNN) pp. 1 - 9
• Main Authors: Rasouli, Peyman, Yu, Ingrid Chieh
• Format: Conference Proceeding Journal Article
• Language: English
• Published: IEEE 01.01.2020
• Subjects: Analytical models; Data models; Data Sampling; Decision trees; Interpretable Machine Learning; Machine learning; Neural networks; Perturbation-based Explanation Methods; Predictive models; Training data; XAI
• ISSN: 2161-4393; 2161-4407
• DOI: 10.1109/IJCNN48605.2020.9206710

Defining a representative locality is an urgent challenge in perturbation-based explanation methods, which influences the fidelity and soundness of explanations. We address this issue by proposing a robust and intuitive approach for EXPLaining black-box classifiers using Adaptive Neighborhood generation (EXPLAN). EXPLAN is a module-based algorithm consisted of dense data generation, representative data selection, data balancing, and rule-based interpretable model. It takes into account the adjacency information derived from the black-box decision function and the structure of the data for creating a representative neighborhood for the instance being explained. As a local model-agnostic explanation method, EXPLAN generates explanations in the form of logical rules that are highly interpretable and well-suited for qualitative analysis of the model's behavior. We discuss fidelity-interpretability trade-offs and demonstrate the performance of the proposed algorithm by a comprehensive comparison with state-of-the-art explanation methods LIME, LORE, and Anchor. The conducted experiments on real-world data sets show our method achieves solid empirical results in terms of fidelity, precision, and stability of explanations.