Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification

• URL: http://arxiv.org/abs/2406.12507v2
• Date: Mon, 12 Aug 2024 14:38:18 GMT
• Title: Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification
• Authors: Davide Italo Serramazza, Thach Le Nguyen, Georgiana Ifrim,
• Abstract summary: We focus on analyzing InterpretTime, a recent evaluation methodology for attribution methods applied to MTSC. We showcase some significant weaknesses of the original methodology and propose ideas to improve its accuracy and efficiency. We find that perturbation-based methods such as SHAP and Feature Ablation work well across a set of datasets.
• Score: 4.588028371034407
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Explanation for Multivariate Time Series Classification (MTSC) is an important topic that is under explored. There are very few quantitative evaluation methodologies and even fewer examples of actionable explanation, where the explanation methods are shown to objectively improve specific computational tasks on time series data. In this paper we focus on analyzing InterpretTime, a recent evaluation methodology for attribution methods applied to MTSC. We showcase some significant weaknesses of the original methodology and propose ideas to improve both its accuracy and efficiency. Unlike related work, we go beyond evaluation and also showcase the actionability of the produced explainer ranking, by using the best attribution methods for the task of channel selection in MTSC. We find that perturbation-based methods such as SHAP and Feature Ablation work well across a set of datasets, classifiers and tasks and outperform gradient-based methods. We apply the best ranked explainers to channel selection for MTSC and show significant data size reduction and improved classifier accuracy.

Related papers

• Supervised Time Series Classification for Anomaly Detection in Subsea Engineering [0.0]
  We investigate the use of supervised machine learning classification algorithms on simulated data based on a physical system with two states: Intact and Broken. We provide a comprehensive discussion of the preprocessing of temporal data, using measures of statistical dispersion and dimension reduction techniques. We conclude with a comparison of the various methods based on different performance metrics, showing the advantage of using machine learning techniques as a tool in decision making.
  arXiv  Detail & Related papers  (2024-03-12T18:25:10Z)
• Robust Analysis of Multi-Task Learning Efficiency: New Benchmarks on Light-Weighed Backbones and Effective Measurement of Multi-Task Learning Challenges by Feature Disentanglement [69.51496713076253]
  In this paper, we focus on the aforementioned efficiency aspects of existing MTL methods. We first carry out large-scale experiments of the methods with smaller backbones and on a the MetaGraspNet dataset as a new test ground. We also propose Feature Disentanglement measure as a novel and efficient identifier of the challenges in MTL.
  arXiv  Detail & Related papers  (2024-02-05T22:15:55Z)
• Majorization-Minimization for sparse SVMs [46.99165837639182]
  Support Vector Machines (SVMs) were introduced for performing binary classification tasks, under a supervised framework, several decades ago. They often outperform other supervised methods and remain one of the most popular approaches in the machine learning arena. In this work, we investigate the training of SVMs through a smooth sparse-promoting-regularized squared hinge loss minimization.
  arXiv  Detail & Related papers  (2023-08-31T17:03:16Z)
• Better Understanding Differences in Attribution Methods via Systematic Evaluations [57.35035463793008]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We use these evaluation schemes to study strengths and shortcomings of some widely used attribution methods over a wide range of models.
  arXiv  Detail & Related papers  (2023-03-21T14:24:58Z)
• Towards Better Understanding Attribution Methods [77.1487219861185]
  Post-hoc attribution methods have been proposed to identify image regions most influential to the models' decisions. We propose three novel evaluation schemes to more reliably measure the faithfulness of those methods. We also propose a post-processing smoothing step that significantly improves the performance of some attribution methods.
  arXiv  Detail & Related papers  (2022-05-20T20:50:17Z)
• Early Time-Series Classification Algorithms: An Empirical Comparison [59.82930053437851]
  Early Time-Series Classification (ETSC) is the task of predicting the class of incoming time-series by observing as few measurements as possible. We evaluate six existing ETSC algorithms on publicly available data, as well as on two newly introduced datasets.
  arXiv  Detail & Related papers  (2022-03-03T10:43:56Z)
• Temporal Dependencies in Feature Importance for Time Series Predictions [4.082348823209183]
  We propose WinIT, a framework for evaluating feature importance in time series prediction settings. We demonstrate how the solution improves the appropriate attribution of features within time steps. WinIT achieves 2.47x better performance than FIT and other feature importance methods on real-world clinical MIMIC-mortality task.
  arXiv  Detail & Related papers  (2021-07-29T20:31:03Z)
• MCDAL: Maximum Classifier Discrepancy for Active Learning [74.73133545019877]
  Recent state-of-the-art active learning methods have mostly leveraged Generative Adversarial Networks (GAN) for sample acquisition. We propose in this paper a novel active learning framework that we call Maximum Discrepancy for Active Learning (MCDAL) In particular, we utilize two auxiliary classification layers that learn tighter decision boundaries by maximizing the discrepancies among them.
  arXiv  Detail & Related papers  (2021-07-23T06:57:08Z)
• Comprehensive Comparative Study of Multi-Label Classification Methods [1.1278903078792917]
  Multi-label classification (MLC) has recently received increasing interest from the machine learning community. This work provides a comprehensive empirical study of a wide range of MLC methods on a plethora of datasets from various domains.
  arXiv  Detail & Related papers  (2021-02-14T09:38:15Z)
• Multitask Learning for Class-Imbalanced Discourse Classification [74.41900374452472]
  We show that a multitask approach can improve 7% Micro F1-score upon current state-of-the-art benchmarks. We also offer a comparative review of additional techniques proposed to address resource-poor problems in NLP.
  arXiv  Detail & Related papers  (2021-01-02T07:13:41Z)

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.