A Unified CatBoost Framework for IoT Data Classification Using Multi-Level Feature Extraction

Chengfeng Qiu, Jiahui Zhou, Yongfeng Liao, Zhengliang Cui, Dan Li

Abstract: Buildings are major energy consumers, and efficient management is crucial for reducing emissions, yet the lack of standardised data formats complicates this process. This paper presents a method for automating the classification of time-series building data, using a segmentation approach to enhance feature extraction and applying hierarchical differential features to capture temporal dynamics. The multi-label classification problem is reformulated into a unified single-label classification task by encoding multiple labels into a discrete 91-class label space. The model, trained with CatBoost and evaluated using 5-fold cross-validation, secured a top ranking in the Brick by Brick 2024 competition, demonstrating its effectiveness in promoting energy-efficient building operations.

External IDs:dblp:conf/www/QiuZLCL25