MAIRE - A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers

The paper introduces a novel framework for extracting model-agnostic human interpretable rules to explain a classifier's output. The human interpretable rule is defined as an axis-aligned hyper-cuboid containing the instance for which the classification decision has to be explained. The proposed procedure finds the largest (high \textit{coverage}) axis-aligned hyper-cuboid such that a high percentage of the instances in the hyper-cuboid have the same class label as the instance being explained (high \textit{precision}). Novel approximations to the coverage and precision measures in terms of the parameters of the hyper-cuboid are defined. They are maximized using gradient-based optimizers. The quality of the approximations is rigorously analyzed theoretically and experimentally. Heuristics for simplifying the generated explanations for achieving better interpretability and a greedy selection algorithm that combines the local explanations for creating global explanations for the model covering a large part of the instance space are also proposed. The framework is model agnostic, can be applied to any arbitrary classifier, and all types of attributes (including continuous, ordered, and unordered discrete). The wide-scale applicability of the framework is validated on a variety of synthetic and real-world datasets from different domains (tabular, text, and image).

[1]  Margo I. Seltzer,et al.  Learning Certifiably Optimal Rule Lists , 2017, KDD.

[2]  Ying Liu,et al.  The Maximum Box Problem and its Application to Data Analysis , 2002, Comput. Optim. Appl..

[3]  Zijian Zhang,et al.  Score-CAM: Improved Visual Explanations Via Score-Weighted Class Activation Mapping , 2019, ArXiv.

[4]  Harish G. Ramaswamy,et al.  Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization , 2020, 2020 IEEE Winter Conference on Applications of Computer Vision (WACV).

[5]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[6]  Thomas Brox,et al.  Striving for Simplicity: The All Convolutional Net , 2014, ICLR.

[7]  Cynthia Rudin,et al.  This Looks Like That: Deep Learning for Interpretable Image Recognition , 2018 .

[8]  Subhas C. Nandy,et al.  Variations of largest rectangle recognition amidst a bichromatic point set , 2019, Discret. Appl. Math..

[9]  Vineeth N. Balasubramanian,et al.  Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[10]  Franco Turini,et al.  Factual and Counterfactual Explanations for Black Box Decision Making , 2019, IEEE Intelligent Systems.

[11]  Zijian Zhang,et al.  Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[12]  Alexander Binder,et al.  On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation , 2015, PloS one.

[13]  Jure Leskovec,et al.  Interpretable Decision Sets: A Joint Framework for Description and Prediction , 2016, KDD.

[14]  Le Song,et al.  Learning to Explain: An Information-Theoretic Perspective on Model Interpretation , 2018, ICML.

[15]  Bolei Zhou,et al.  Learning Deep Features for Discriminative Localization , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Jure Leskovec,et al.  Faithful and Customizable Explanations of Black Box Models , 2019, AIES.

[17]  Avanti Shrikumar,et al.  Learning Important Features Through Propagating Activation Differences , 2017, ICML.

[18]  Stan Matwin,et al.  Black Box Explanation by Learning Image Exemplars in the Latent Feature Space , 2019, ECML/PKDD.

[19]  Quanshi Zhang,et al.  Interpretable Convolutional Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[20]  Carlos Guestrin,et al.  Anchors: High-Precision Model-Agnostic Explanations , 2018, AAAI.

[21]  Ovidiu Daescu,et al.  Maximum Area Rectangle Separating Red and Blue Points , 2016, CCCG.

[22]  Cynthia Rudin,et al.  Interpretable Image Recognition with Hierarchical Prototypes , 2019, HCOMP.

[23]  Scott Lundberg,et al.  A Unified Approach to Interpreting Model Predictions , 2017, NIPS.

[24]  Cynthia Rudin,et al.  Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions , 2017, AAAI.

[25]  Carlos Guestrin,et al.  "Why Should I Trust You?": Explaining the Predictions of Any Classifier , 2016, ArXiv.

[26]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.