PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology

• URL: http://arxiv.org/abs/2504.18329v1
• Date: Fri, 25 Apr 2025 13:14:11 GMT
• Title: PHEATPRUNER: Interpretable Data-centric Feature Selection for Multivariate Time Series Classification through Persistent Homology
• Authors: Anh-Duy Pham, Olivier Basole Kashongwe, Martin Atzmueller, Tim Römer,
• Abstract summary: PHeatPruner is a method to balance performance and interpretability in time series classification.<n> Persistent homology facilitates the pruning of up to 45% of the applied variables.<n>Sheaf theory contributes explanatory vectors that provide deeper insights into the data's structural nuances.
• Score: 0.0937465283958018
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Balancing performance and interpretability in multivariate time series classification is a significant challenge due to data complexity and high dimensionality. This paper introduces PHeatPruner, a method integrating persistent homology and sheaf theory to address these challenges. Persistent homology facilitates the pruning of up to 45% of the applied variables while maintaining or enhancing the accuracy of models such as Random Forest, CatBoost, XGBoost, and LightGBM, all without depending on posterior probabilities or supervised optimization algorithms. Concurrently, sheaf theory contributes explanatory vectors that provide deeper insights into the data's structural nuances. The approach was validated using the UEA Archive and a mastitis detection dataset for dairy cows. The results demonstrate that PHeatPruner effectively preserves model accuracy. Furthermore, our results highlight PHeatPruner's key features, i.e. simplifying complex data and offering actionable insights without increasing processing time or complexity. This method bridges the gap between complexity reduction and interpretability, suggesting promising applications in various fields.

Related papers

• Optimizing VarLiNGAM for Scalable and Efficient Time Series Causal Discovery [5.430532390358285]
  Causal discovery is designed to identify causal relationships in data. Time series causal discovery is particularly challenging due to the need to account for temporal dependencies and potential time lag effects. This study significantly improves the feasibility of processing large datasets.
  arXiv  Detail & Related papers  (2024-09-09T10:52:58Z)
• Discovering physical laws with parallel combinatorial tree search [57.05912962368898]
  Symbolic regression plays a crucial role in scientific research thanks to its capability of discovering concise and interpretable mathematical expressions from data.<n>Existing algorithms have faced a critical bottleneck of accuracy and efficiency over a decade.<n>We introduce a parallel tree search (PCTS) model to efficiently distill generic mathematical expressions from limited data.
  arXiv  Detail & Related papers  (2024-07-05T10:41:15Z)
• Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series [45.76310830281876]
  We propose Quantile Sub-Ensembles, a novel method to estimate uncertainty with ensemble of quantile-regression-based task networks. Our method not only produces accurate imputations that is robust to high missing rates, but also is computationally efficient due to the fast training of its non-generative model.
  arXiv  Detail & Related papers  (2023-12-03T05:52:30Z)
• Latent Processes Identification From Multi-View Time Series [17.33428123777779]
  We propose a novel framework that employs the contrastive learning technique to invert the data generative process for enhanced identifiability. MuLTI integrates a permutation mechanism that merges corresponding overlapped variables by the establishment of an optimal transport formula.
  arXiv  Detail & Related papers  (2023-05-14T14:21:58Z)
• Amortized Inference for Causal Structure Learning [72.84105256353801]
  Learning causal structure poses a search problem that typically involves evaluating structures using a score or independence test. We train a variational inference model to predict the causal structure from observational/interventional data. Our models exhibit robust generalization capabilities under substantial distribution shift.
  arXiv  Detail & Related papers  (2022-05-25T17:37:08Z)
• PIETS: Parallelised Irregularity Encoders for Forecasting with Heterogeneous Time-Series [5.911865723926626]
  Heterogeneity and irregularity of multi-source data sets present a significant challenge to time-series analysis. In this work, we design a novel architecture, PIETS, to model heterogeneous time-series. We show that PIETS is able to effectively model heterogeneous temporal data and outperforms other state-of-the-art approaches in the prediction task.
  arXiv  Detail & Related papers  (2021-09-30T20:01:19Z)
• Generalizable Mixed-Precision Quantization via Attribution Rank Preservation [90.26603048354575]
  We propose a generalizable mixed-precision quantization (GMPQ) method for efficient inference. Our method obtains competitive accuracy-complexity trade-off compared with the state-of-the-art mixed-precision networks.
  arXiv  Detail & Related papers  (2021-08-05T16:41:57Z)
• Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors [14.337297795182181]
  Gaussian processes (GP) have difficulties in accommodating big datasets, categorical inputs, and multiple responses. We propose a GP model that utilizes latent variables and functions obtained through variational inference to address the aforementioned challenges simultaneously. Our approach is demonstrated for machine learning of ternary oxide materials and topology optimization of a multiscale compliant mechanism.
  arXiv  Detail & Related papers  (2021-06-26T02:17:23Z)
• Interpretable Feature Construction for Time Series Extrinsic Regression [0.028675177318965035]
  In some application domains, it occurs that the target variable is numerical and the problem is known as time series extrinsic regression (TSER) We suggest an extension of a Bayesian method for robust and interpretable feature construction and selection in the context of TSER. Our approach exploits a relational way to tackle with TSER: (i), we build various and simple representations of the time series which are stored in a relational data scheme, then, (ii), a propositionalisation technique is applied to build interpretable features from secondary tables to "flatten" the data.
  arXiv  Detail & Related papers  (2021-03-15T08:12:19Z)
• Sparse PCA via $l_{2,p}$-Norm Regularization for Unsupervised Feature Selection [138.97647716793333]
  We propose a simple and efficient unsupervised feature selection method, by combining reconstruction error with $l_2,p$-norm regularization. We present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically.
  arXiv  Detail & Related papers  (2020-12-29T04:08:38Z)
• Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift [81.74795324629712]
  We call prediction-time batch normalization, which significantly improves model accuracy and calibration under covariate shift. We show that prediction-time batch normalization provides complementary benefits to existing state-of-the-art approaches for improving robustness. The method has mixed results when used alongside pre-training, and does not seem to perform as well under more natural types of dataset shift.
  arXiv  Detail & Related papers  (2020-06-19T05:08:43Z)
• Transformer Hawkes Process [79.16290557505211]
  We propose a Transformer Hawkes Process (THP) model, which leverages the self-attention mechanism to capture long-term dependencies. THP outperforms existing models in terms of both likelihood and event prediction accuracy by a notable margin. We provide a concrete example, where THP achieves improved prediction performance for learning multiple point processes when incorporating their relational information.
  arXiv  Detail & Related papers  (2020-02-21T13:48:13Z)

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.