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

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.

Hoeffding adaptive trees for multi-label classification on data streams

Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims at developing models that can process instances as they arrive, so models constantly adapt to new concepts and the temporal evolution in the stream. In multi-label data stream environments where instances have the peculiarity of belonging simultaneously to more than one class, the problem becomes even more complex and poses unique challenges such as different concept drifts impacting different labels at simultaneous or distinct times, higher class imbalance, or new labels emerging in the stream. This paper proposes a novel approach to multi-label data stream classification called Multi-Label Hoeffding Adaptive Tree (MLHAT). MLHAT leverages the Hoeffding adaptive tree to address these challenges by considering possible relations and label co-occurrences in the partitioning process of the decision tree, dynamically adapting the learner in each leaf node of the tree, and implementing a concept drift detector that can quickly detect and replace tree branches that are no longer performing well. The proposed approach is compared with other 18 online multi-label classifiers on 41 datasets. The results, validated with statistical analysis, show that MLHAT outperforms other state-of-the-art approaches in 12 well-known multi-label metrics.