A Decision Tree is a supervised machine learning method that recursively partitions the feature space into hierarchical branches based on threshold conditions applied to input features, ultimately assigning class labels or regression values at terminal leaf nodes. In WiFi/CSI and RF-based sensing research, Decision Trees are valued for their interpretability and low computational overhead, making them practical for real-time occupancy detection and crowd modeling tasks where transparency in decision-making is important. Key variants include single Decision Trees, which are prone to overfitting, and ensemble extensions such as Random Forests and Gradient Boosted Trees, which improve generalization by aggregating predictions across multiple trees trained on different subsets of the data.
Source Papers
- A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open Issues ↗ — A Survey on Wireless Device-free Human Sensing: Application
- BLE Can See: A Reinforcement Learning Approach for RF-based Indoor Occupancy Detection ↗ — BLE Can See: A Reinforcement Learning Approach for RF-based
- Data-driven Crowd Modeling Techniques: A Survey ↗ — Data-driven Crowd Modeling Techniques: A Survey
- Device-Free Passive Identity Identification via WiFi Signals ↗ — Device-Free Passive Identity Identification via WiFi Signals
- Efficient machine learning for Wi-Fi CSI-based human activity recognition using fast Monte Carlo based feature extraction ↗ — Efficient machine learning for Wi-Fi CSI-based human activit
- Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning ↗ — Human Activity Recognition via Wi-Fi and Inertial Sensors Wi
- Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions ↗ — Occupancy Prediction in IoT-Enabled Smart Buildings: Technol
- WiFi Sensing with Channel State Information ↗ — WiFi Sensing with Channel State Information