DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices
This addresses the need for efficient online learning on resource-constrained IoT edge devices, though it appears incremental as it builds on existing hoeffding tree approaches.
The paper tackles the problem of energy-efficient data stream mining on edge devices by proposing DFDT, a dynamic fast decision tree algorithm that adjusts parameters based on incoming data. Experiments show it achieves higher predictive performance (0.43 vs 0.29 ranking) with constrained memory and reduced runtime compared to existing methods.
The Internet of Things generates massive data streams, with edge computing emerging as a key enabler for online IoT applications and 5G networks. Edge solutions facilitate real-time machine learning inference, but also require continuous adaptation to concept drifts. Ensemble-based solutions improve predictive performance, but incur higher resource consumption, latency, and memory demands. This paper presents DFDT: Dynamic Fast Decision Tree, a novel algorithm designed for energy-efficient memory-constrained data stream mining. DFDT improves hoeffding tree growth efficiency by dynamically adjusting grace periods, tie thresholds, and split evaluations based on incoming data. It incorporates stricter evaluation rules (based on entropy, information gain, and leaf instance count), adaptive expansion modes, and a leaf deactivation mechanism to manage memory, allowing more computation on frequently visited nodes while conserving energy on others. Experiments show that the proposed framework can achieve increased predictive performance (0.43 vs 0.29 ranking) with constrained memory and a fraction of the runtime of VFDT or SVFDT.