Speaker
Description
In the era of continuous data generation, real-time processing of data streams has become crucial for timely, adaptive, and context-aware decision-making. However, maintaining effective learning models in such dynamic environments requires carefully balancing prediction performance, transparency and energy consumption.
In the talk, we will present two new state-of-the-art methods for classification on data streams in such settings: (i) SoHoTs (soft Hoeffding trees) for balancing prediction performance and transparency, and (ii) HEROS (Heterogeneous Online Ensembles) for balancing prediction performance and energy consumption. SoHoTs are transparent, differentiable decision tree models for data streams. They employ a novel routing mechanism based on the Hoeffding inequality and adapt to changing data distributions through gradient-based weight updates, like soft decision trees. They process data in real-time, one sample at a time, without the need for storage, and enhance interpretability via decision-rule-based feature importance, sparse activation, and visualized decision paths. To study the trade-off between prediction performance and energy consumption, we introduce HEROS, which avoid expensive hyperparameter optimization by maintaining a diverse pool of preconfigured models. At each time step, HEROS select a resource-aware subset of models for training. A novel zeta-policy is introduced to guide this selection process, prioritizing models that deliver near-optimal performance under strict resource constraints. Empirical evaluations across 20 data streams (SoHoTs) and 11 benchmark datasets (HEROS) demonstrate that both methods achieve strong predictive performance while ensuring transparency or reduced resource consumption.