TimeSHAP: Explaining Recurrent Models through Sequence Perturbations

• URL: http://arxiv.org/abs/2012.00073v1
• Date: Mon, 30 Nov 2020 19:48:57 GMT
• Title: TimeSHAP: Explaining Recurrent Models through Sequence Perturbations
• Authors: Jo\~ao Bento, Pedro Saleiro, Andr\'e F. Cruz, M\'ario A.T. Figueiredo, Pedro Bizarro
• Abstract summary: Recurrent neural networks are a standard building block in numerous machine learning domains. The complex decision-making in these models is seen as a black-box, creating a tension between accuracy and interpretability. In this work, we contribute to filling these gaps by presenting TimeSHAP, a model-agnostic recurrent explainer.
• Score: 3.1498833540989413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recurrent neural networks are a standard building block in numerous machine learning domains, from natural language processing to time-series classification. While their application has grown ubiquitous, understanding of their inner workings is still lacking. In practice, the complex decision-making in these models is seen as a black-box, creating a tension between accuracy and interpretability. Moreover, the ability to understand the reasoning process of a model is important in order to debug it and, even more so, to build trust in its decisions. Although considerable research effort has been guided towards explaining black-box models in recent years, recurrent models have received relatively little attention. Any method that aims to explain decisions from a sequence of instances should assess, not only feature importance, but also event importance, an ability that is missing from state-of-the-art explainers. In this work, we contribute to filling these gaps by presenting TimeSHAP, a model-agnostic recurrent explainer that leverages KernelSHAP's sound theoretical footing and strong empirical results. As the input sequence may be arbitrarily long, we further propose a pruning method that is shown to dramatically improve its efficiency in practice.

Related papers

• Even-if Explanations: Formal Foundations, Priorities and Complexity [18.126159829450028]
  We show that both linear and tree-based models are strictly more interpretable than neural networks. We introduce a preference-based framework that enables users to personalize explanations based on their preferences.
  arXiv  Detail & Related papers  (2024-01-17T11:38:58Z)
• On the Consistency and Robustness of Saliency Explanations for Time Series Classification [4.062872727927056]
  Saliency maps have been applied to interpret time series windows as images. This paper extensively analyzes the consistency and robustness of saliency maps for time series features and temporal attribution.
  arXiv  Detail & Related papers  (2023-09-04T09:08:22Z)
• Neural Causal Models for Counterfactual Identification and Estimation [62.30444687707919]
  We study the evaluation of counterfactual statements through neural models. First, we show that neural causal models (NCMs) are expressive enough. Second, we develop an algorithm for simultaneously identifying and estimating counterfactual distributions.
  arXiv  Detail & Related papers  (2022-09-30T18:29:09Z)
• Shapelet-Based Counterfactual Explanations for Multivariate Time Series [0.9990687944474738]
  We develop a model agnostic multivariate time series (MTS) counterfactual explanation algorithm. We test our approach on a real-life solar flare prediction dataset and prove that our approach produces high-quality counterfactuals. In addition to being visually interpretable, our explanations are superior in terms of proximity, sparsity, and plausibility.
  arXiv  Detail & Related papers  (2022-08-22T17:33:31Z)
• Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations [97.91630330328815]
  We conduct a crowdsourcing study, where participants interact with deception detection models that have been trained to distinguish between genuine and fake hotel reviews. We observe that for a linear bag-of-words model, participants with access to the feature coefficients during training are able to cause a larger reduction in model confidence in the testing phase when compared to the no-explanation control.
  arXiv  Detail & Related papers  (2021-12-17T18:29:56Z)
• Leveraging the structure of dynamical systems for data-driven modeling [111.45324708884813]
  We consider the impact of the training set and its structure on the quality of the long-term prediction. We show how an informed design of the training set, based on invariants of the system and the structure of the underlying attractor, significantly improves the resulting models.
  arXiv  Detail & Related papers  (2021-12-15T20:09:20Z)
• Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification [0.0]
  We propose an LTCN-based model for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. Our interpretable model obtains competitive performance when compared to the state-of-the-art white and black boxes.
  arXiv  Detail & Related papers  (2021-07-07T18:14:50Z)
• Closed-form Continuous-Depth Models [99.40335716948101]
  Continuous-depth neural models rely on advanced numerical differential equation solvers. We present a new family of models, termed Closed-form Continuous-depth (CfC) networks, that are simple to describe and at least one order of magnitude faster.
  arXiv  Detail & Related papers  (2021-06-25T22:08:51Z)
• Towards a Rigorous Evaluation of Explainability for Multivariate Time Series [5.786452383826203]
  This study was to achieve and evaluate model agnostic explainability in a time series forecasting problem. The solution involved framing the problem as a time series forecasting problem to predict the sales deals. The explanations produced by LIME and SHAP greatly helped lay humans in understanding the predictions made by the machine learning model.
  arXiv  Detail & Related papers  (2021-04-06T17:16:36Z)
• Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations [64.85696493596821]
  In computer vision applications, generative counterfactual methods indicate how to perturb a model's input to change its prediction. We propose a counterfactual method that learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss. Our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-18T12:57:34Z)
• Plausible Counterfactuals: Auditing Deep Learning Classifiers with Realistic Adversarial Examples [84.8370546614042]
  Black-box nature of Deep Learning models has posed unanswered questions about what they learn from data. Generative Adversarial Network (GAN) and multi-objectives are used to furnish a plausible attack to the audited model. Its utility is showcased within a human face classification task, unveiling the enormous potential of the proposed framework.
  arXiv  Detail & Related papers  (2020-03-25T11:08:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.