TY - CPAPER U1 - Konferenzveröffentlichung A1 - Shajalal, Md A1 - Bohlouli, Milad A1 - Das, Hari Prasanna A1 - Boden, Alexander A1 - Stevens, Gunnar T1 - Focus on what matters: improved feature selection techniques for personal thermal comfort modelling T2 - BuildSys '22: The 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation. Boston, Massachusetts, November 9 - 10, 2022 N2 - Occupants' personal thermal comfort (PTC) is indispensable for their well-being, physical and mental health, and work efficiency. Predicting PTC preferences in a smart home can be a prerequisite to adjusting the indoor temperature for providing a comfortable environment. In this research, we focus on identifying relevant features for predicting PTC preferences. We propose a machine learning-based predictive framework by employing supervised feature selection techniques. We apply two feature selection techniques to select the optimal sets of features to improve the thermal preference prediction performance. The experimental results on a public PTC dataset demonstrated the efficiency of the feature selection techniques that we have applied. In turn, our PTC prediction framework with feature selection techniques achieved state-of-the-art performance in terms of accuracy, Cohen's kappa, and area under the curve (AUC), outperforming conventional methods. KW - thermal comfort modelling KW - Machine Learning KW - feature selection Y1 - 2022 SN - 978-1-4503-9890-9 SB - 978-1-4503-9890-9 U6 - https://doi.org/10.1145/3563357.3567406 DO - https://doi.org/10.1145/3563357.3567406 SP - 496 EP - 499 S1 - 4 PB - Association for Computing Machinery CY - New York, NY, United States ER -