XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification

• URL: http://arxiv.org/abs/2005.03645v5
• Date: Tue, 15 Feb 2022 10:15:35 GMT
• Title: XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification
• Authors: Kevin Fauvel, \'Elisa Fromont, V\'eronique Masson, Philippe Faverdin, Alexandre Termier
• Abstract summary: We present XEM, an eXplainable-by-design Ensemble method for Multivariable time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach and an implicit divide-and-conquer approach. Our evaluation shows that XEM outperforms the state-of-the-art MTS classifiers on the public UEA datasets.
• Score: 61.33695273474151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present XEM, an eXplainable-by-design Ensemble method for Multivariate time series classification. XEM relies on a new hybrid ensemble method that combines an explicit boosting-bagging approach to handle the bias-variance trade-off faced by machine learning models and an implicit divide-and-conquer approach to individualize classifier errors on different parts of the training data. Our evaluation shows that XEM outperforms the state-of-the-art MTS classifiers on the public UEA datasets. Furthermore, XEM provides faithful explainability-by-design and manifests robust performance when faced with challenges arising from continuous data collection (different MTS length, missing data and noise).

Related papers

• Adaptive Transfer Clustering: A Unified Framework [2.3144964550307496]
  We propose an adaptive transfer clustering (ATC) algorithm that automatically leverages the commonality in the presence of unknown discrepancy. It applies to a broad class of statistical models including Gaussian mixture models, block models, and latent class models.
  arXiv  Detail & Related papers  (2024-10-28T17:57:06Z)
• Dynamical mixture modeling with fast, automatic determination of Markov chains [0.0]
  Variational EM efficiently identifies the number of Markov chains and dynamics of each chain without expensive model comparisons or posterior sampling. The approach is supported by a theoretical analysis and numerical experiments, including simulated and observational data sets based on $tt Last.fm$ music listening, ultramarathon running, and gene expression.
  arXiv  Detail & Related papers  (2024-06-07T05:43:11Z)
• Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series [7.5986411724707095]
  We propose a novel class of models, a mixture of coupled hidden Markov models (M-CHMM) To make the model learning feasible, we derive two algorithms to sample the sequences of the latent variables in the CHMM. Compared to existing inference methods, our algorithms are computationally tractable, improve mixing, and allow for likelihood estimation.
  arXiv  Detail & Related papers  (2023-11-14T02:55:37Z)
• MHCCL: Masked Hierarchical Cluster-Wise Contrastive Learning for Multivariate Time Series [20.008535430484475]
  Masked Hierarchical Cluster-wise Contrastive Learning model is presented. It exploits semantic information obtained from the hierarchical structure consisting of multiple latent partitions for time series. It is shown to be superior to state-of-the-art approaches for unsupervised time series representation learning.
  arXiv  Detail & Related papers  (2022-12-02T12:42:53Z)
• Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality [84.94877848357896]
  Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. We analyze this behavior of seq2seq models and identify two contributing factors: a lack of mutual exclusivity bias and the tendency to memorize whole examples. We show substantial empirical improvements using standard sequence-to-sequence models on two widely-used compositionality datasets.
  arXiv  Detail & Related papers  (2022-11-28T17:36:41Z)
• Meta-Causal Feature Learning for Out-of-Distribution Generalization [71.38239243414091]
  This paper presents a balanced meta-causal learner (BMCL), which includes a balanced task generation module (BTG) and a meta-causal feature learning module (MCFL) BMCL effectively identifies the class-invariant visual regions for classification and may serve as a general framework to improve the performance of the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-08-22T09:07:02Z)
• Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling [54.94763543386523]
  Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the ( aggregate) posterior to encourage statistical independence of the latent factors. We present a novel multi-stage modeling approach where the disentangled factors are first learned using a penalty-based disentangled representation learning method. Then, the low-quality reconstruction is improved with another deep generative model that is trained to model the missing correlated latent variables.
  arXiv  Detail & Related papers  (2020-10-25T18:51:15Z)
• Robust Finite Mixture Regression for Heterogeneous Targets [70.19798470463378]
  We propose an FMR model that finds sample clusters and jointly models multiple incomplete mixed-type targets simultaneously. We provide non-asymptotic oracle performance bounds for our model under a high-dimensional learning framework. The results show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2020-10-12T03:27:07Z)
• Model Agnostic Combination for Ensemble Learning [0.0]
  We present a novel ensembling technique coined MAC that is designed to find the optimal function for combining models. Being agnostic to the number of sub-models enables addition and replacement of sub-models to the combination even after deployment.
  arXiv  Detail & Related papers  (2020-06-16T09:44:58Z)
• Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data [75.94147344921355]
  We build a multi-branch ensemble framework by using diverse feature transformation matrices. We propose a data blending method to exploit the unlabeled data and augment the sparse support set in the target domain.
  arXiv  Detail & Related papers  (2020-06-08T02:27:34Z)

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.