让决策树模型瘦身4-16倍，能在低功耗设备上独立运行。
Boosted Trees on a Diet: Compact Models for Resource-Constrained Devices
- 训练时鼓励特征和阈值复用，减少模型冗余。
- 相比LightGBM，性能不变但压缩率达4-16倍。
- 适合远程监控、边缘计算等资源受限场景。
在计算资源受限的设备上部署机器学习模型已成为现代物联网应用的关键。本文提出一种提升决策树的压缩方案，应对轻量化模型的迫切需求。具体而言，我们设计了训练紧凑型提升决策树集成的方法，通过奖励训练过程中特征与阈值的复用，显著降低内存占用。实验表明，采用改进训练流程和替代内存布局后，模型在保持相同性能的前提下，压缩比达到4-16倍，相较于LightGBM。部署后，对应物联网设备可脱离持续通信或外部供电，实现自主运行，仅需极低算力与能耗。该能力为远程监测、边缘分析及孤岛环境中的实时决策等广泛应用打开了可能。
原文摘要 · Abstract (English)
Deploying machine learning models on compute-constrained devices has become a key building block of modern IoT applications. In this work, we present a compression scheme for boosted decision trees, addressing the growing need for lightweight machine learning models. Specifically, we provide techniques for training compact boosted decision tree ensembles that exhibit a reduced memory footprint by rewarding, among other things, the reuse of features and thresholds during training. Our experimental evaluation shows that models achieved the same performance with a compression ratio of 4-16x compared to LightGBM models using an adapted training process and an alternative memory layout. Once deployed, the corresponding IoT devices can operate independently of constant communication or external energy supply, and, thus, autonomously, requiring only minimal computing power and energy. This capability opens the door to a wide range of IoT applications, including remote monitoring, edge analytics, and real-time decision making in isolated or power-limited environments.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。