MIHT: A Hoeffding Tree for Time Series Classification using Multiple Instance Learning
This provides a more efficient and interpretable solution for time series classification in domains with complex, dynamic data, though it is an incremental improvement over existing methods.
The paper tackles the problem of classifying multivariate and variable-length time series by introducing the MIHT algorithm, which outperforms 11 state-of-the-art models on 28 public datasets with enhanced accuracy and interpretability.
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.