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

2026-03-23

AI Summary

This work addresses the challenges of modeling and interpretation in multivariate, variable-length time series classification, where high dimensionality and heterogeneous sequence lengths complicate learning. The authors propose a multi-instance learning approach based on a “bag-of-subsequences” representation, which—by integrating Hoeffding trees with multi-instance learning for the first time—yields an incremental, white-box decision model. This method effectively discriminates between informative temporal segments and noise, automatically identifying salient variables and critical time intervals while maintaining model compactness and enhancing interpretability. Experimental evaluation on 28 public datasets demonstrates that the proposed approach outperforms 11 state-of-the-art algorithms in overall accuracy, with particularly strong performance in high-dimensional settings.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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 11 state-of-the-art time series classification models on 28 public datasets, including high-dimensional ones. MIHT offers enhanced accuracy and interpretability, making it a promising solution for handling complex, dynamic time series data.

Problem

• time series classification
• variable-length
• high dimensionality
• multivariate time series

Innovation

• Multiple Instance Learning
• Hoeffding Tree
• Time Series Classification
• Interpretable Model
• Variable-length Time Series