Ensemble Interpretation: A Unified Method for Interpretable Machine Learning

• URL: http://arxiv.org/abs/2312.06255v1
• Date: Mon, 11 Dec 2023 09:51:24 GMT
• Title: Ensemble Interpretation: A Unified Method for Interpretable Machine Learning
• Authors: Chao Min, Guoyong Liao, Guoquan Wen, Yingjun Li, Xing Guo
• Abstract summary: A novel interpretable methodology, ensemble interpretation, is presented in this paper. Experiment results show that the ensemble interpretation is more stable and more consistent with human experience and cognition. As an application, we use the ensemble interpretation for feature selection, and then the generalization performance of the corresponding learning model is significantly improved.
• Score: 1.276129213205911
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: To address the issues of stability and fidelity in interpretable learning, a novel interpretable methodology, ensemble interpretation, is presented in this paper which integrates multi-perspective explanation of various interpretation methods. On one hand, we define a unified paradigm to describe the common mechanism of different interpretation methods, and then integrate the multiple interpretation results to achieve more stable explanation. On the other hand, a supervised evaluation method based on prior knowledge is proposed to evaluate the explaining performance of an interpretation method. The experiment results show that the ensemble interpretation is more stable and more consistent with human experience and cognition. As an application, we use the ensemble interpretation for feature selection, and then the generalization performance of the corresponding learning model is significantly improved.

Related papers

• Diffexplainer: Towards Cross-modal Global Explanations with Diffusion Models [51.21351775178525]
  DiffExplainer is a novel framework that, leveraging language-vision models, enables multimodal global explainability. It employs diffusion models conditioned on optimized text prompts, synthesizing images that maximize class outputs. The analysis of generated visual descriptions allows for automatic identification of biases and spurious features.
  arXiv  Detail & Related papers  (2024-04-03T10:11:22Z)
• Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors [3.4253416336476246]
  We focus on the analysis of counterfactual, contrastive explanations. We propose a new back translation-inspired evaluation methodology. We show that by iteratively feeding the counterfactual to the explainer we can obtain valuable insights into the behaviour of both the predictor and the explainer models.
  arXiv  Detail & Related papers  (2023-05-26T16:04:28Z)
• Unsupervised Interpretable Basis Extraction for Concept-Based Visual Explanations [53.973055975918655]
  We show that, intermediate layer representations become more interpretable when transformed to the bases extracted with our method. We compare the bases extracted with our method with the bases derived with a supervised approach and find that, in one aspect, the proposed unsupervised approach has a strength that constitutes a limitation of the supervised one and give potential directions for future research.
  arXiv  Detail & Related papers  (2023-03-19T00:37:19Z)
• On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation [23.72825603188359]
  We can improve the interpretability of explanations by allowing arbitrary text sequences as the explanation unit. We propose a semantic-based evaluation metric that can better align with humans' judgment of explanations.
  arXiv  Detail & Related papers  (2021-06-09T00:49:56Z)
• On the Faithfulness Measurements for Model Interpretations [100.2730234575114]
  Post-hoc interpretations aim to uncover how natural language processing (NLP) models make predictions. To tackle these issues, we start with three criteria: the removal-based criterion, the sensitivity of interpretations, and the stability of interpretations. Motivated by the desideratum of these faithfulness notions, we introduce a new class of interpretation methods that adopt techniques from the adversarial domain.
  arXiv  Detail & Related papers  (2021-04-18T09:19:44Z)
• Interpretable Deep Learning: Interpretations, Interpretability, Trustworthiness, and Beyond [49.93153180169685]
  We introduce and clarify two basic concepts-interpretations and interpretability-that people usually get confused. We elaborate the design of several recent interpretation algorithms, from different perspectives, through proposing a new taxonomy. We summarize the existing work in evaluating models' interpretability using "trustworthy" interpretation algorithms.
  arXiv  Detail & Related papers  (2021-03-19T08:40:30Z)
• Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability [54.85658598523915]
  We propose to have a concrete definition of interpretation before we could evaluate faithfulness of an interpretation. We find that although interpretation methods perform differently under a certain evaluation metric, such a difference may not result from interpretation quality or faithfulness.
  arXiv  Detail & Related papers  (2020-09-16T06:38:03Z)
• On quantitative aspects of model interpretability [0.0]
  We argue that methods along these dimensions can be imputed to two conceptual parts, namely the extractor and the actual explainability method. We experimentally validate our metrics on different benchmark tasks and show how they can be used to guide a practitioner in the selection of the most appropriate method for the task at hand.
  arXiv  Detail & Related papers  (2020-07-15T10:05:05Z)
• Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition [48.06319154279427]
  We present a method of instance-based learning that learns similarities between spans. Our method enables to build models that have high interpretability without sacrificing performance.
  arXiv  Detail & Related papers  (2020-04-29T23:32:42Z)

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.