[2603.22074] MIHT: A Hoeffding Tree for Time Series Classification using Multiple Instance Learning

Computer Science > Machine Learning arXiv:2603.22074 (cs) [Submitted on 23 Mar 2026] Title:MIHT: A Hoeffding Tree for Time Series Classification using Multiple Instance Learning Authors:Aurora Esteban, Amelia Zafra, Sebastián Ventura View a PDF of the paper titled MIHT: A Hoeffding Tree for Time Series Classification using Multiple Instance Learning, by Aurora Esteban and 2 other authors View PDF HTML (experimental) Abstract:Due to the prevalence of temporal data and its inherent dependencies in many real-world problems, time series classification is of paramount importance in various domains. However, existing models often struggle with series of variable length or high dimensionality. This paper introduces the MIHT (Multi-instance Hoeffding Tree) algorithm, an efficient model that uses multi-instance learning to classify multivariate and variable-length time series while providing interpretable results. The algorithm uses a novel representation of time series as "bags of subseries," together with an optimization process based on incremental decision trees that distinguish relevant parts of the series from noise. This methodology extracts the underlying concept of series with multiple variables and variable lengths. The generated decision tree is a compact, white-box representation of the series' concept, providing interpretability insights into the most relevant variables and segments of the series. Experimental results demonstrate MIHT's superiority, as it outperforms 1...