Work

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

Year: 2025

Type: conference-paper

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.

DOI: 10.1145/3701716.3718479

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

Institutions: Sun Yat-sen University, University of Electronic Science and Technology of China, Southwest Jiaotong University

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: gold

OpenAlex ID: W4410637828

FWCI: 7.217

Cites: 3

Cited by: 2

Related to: 10