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

arXiv: https://arxiv.org/abs/2502.14011 · HTML: https://mlsanity.com/paper/2502.14011/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 4 of 4)

- [Challenges in Benchmarking Stream Learning Algorithms with Real-world Data](https://mlsanity.com/paper/2005.00113/) (2020)
- [Extremely Fast Decision Tree](https://mlsanity.com/paper/1802.08780/) (2018)
- [Strict Very Fast Decision Tree: a memory conservative algorithm for data stream mining](https://mlsanity.com/paper/1805.06368/) (2018)
- [Hoeffding Trees with nmin adaptation](https://mlsanity.com/paper/1808.01145/) (2018)

## Built on by (most cited first, 9 of 9)

- [In-context Learning of Evolving Data Streams with Tabular Foundational Models](https://mlsanity.com/paper/2502.16840/) (2025)
- [On-device edge learning for IoT data streams: a survey](https://mlsanity.com/paper/2502.17788/) (2025)
- [Bridging Streaming Continual Learning via In-Context Large Tabular Models](https://mlsanity.com/paper/2512.11668/) (2025)
- [Explainable Anomaly Detection for Industrial IoT Data Streams](https://mlsanity.com/paper/2512.08885/) (2025)
- [Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles](https://mlsanity.com/paper/2605.12803/) (2026)
- [Axle Sensor Fusion for Online Continual Wheel Fault Detection in Wayside Railway Monitoring](https://mlsanity.com/paper/2602.16101/) (2026)
- [Streaming Hierarchical Inference with Tabular Foundation Models](https://mlsanity.com/paper/2609.07956/) (2026)
- [What changes after deployment? A survey on On-device Learning in TinyML](https://mlsanity.com/paper/2605.31226/) (2026)
- [Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus](https://mlsanity.com/paper/2609.23883/) (2026)

## Abstract (arXiv)

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. While extensions of the Very Fast Decision Tree (VFDT) remain state-of-the-art for tabular stream mining, their unregulated growth limits efficiency, particularly in ensemble settings where post-pruning at the individual tree level is seldom applied. This paper presents DFDT, a novel memory-constrained algorithm for online learning. DFDT employs activity-aware pre-pruning, dynamically adjusting splitting criteria based on leaf node activity: low-activity nodes are deactivated to conserve resources, moderately active nodes split under stricter conditions, and highly active nodes leverage a skipping mechanism for accelerated growth. Additionally, adaptive grace periods and tie thresholds allow DFDT to modulate splitting decisions based on observed data variability, enhancing the accuracy-memory-runtime trade-off while minimizing the need for hyperparameter tuning. An ablation study reveals three DFDT variants suited to different resource profiles. Fully compatible with existing ensemble frameworks, DFDT provides a drop-in alternative to standard VFDT-based learners.