DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices

2025-02-19

AI Summary

To address the challenges of resource constraints and frequent concept drift in edge IoT data streams, this paper proposes a memory-aware dynamic Hoeffding tree for online learning. Conventional Very Fast Decision Tree (VFDT)-style algorithms suffer from poor adaptability, high energy consumption, and degraded prediction accuracy under stringent memory limits. To overcome these limitations, we introduce three novel mechanisms: (1) dynamic adjustment of the grace period, split threshold, and evaluation frequency; (2) a dual-constraint splitting criterion based on entropy and information gain; and (3) adaptive leaf node expansion coupled with dynamic deactivation. The proposed method achieves low memory footprint and low latency while significantly enhancing concept drift adaptation. On standard benchmarks, it attains a prediction accuracy of 0.43—representing a 48% improvement over VFDT and SVFDT—and substantially reduces runtime. This work provides an efficient and robust solution for real-time streaming learning at the edge.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Stream Mining
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Efficient data stream mining
• Energy and memory optimization
• Adaptive decision tree algorithm

Innovation

• Dynamic Fast Decision Tree
• Adaptive expansion modes
• Leaf deactivation mechanism