Classification in Early Fire Detection Using Transfer Learning Based on Multi-Sensor Nodes

Multi-sensor data from a small laboratory setup was used to create a new feature space with linear discriminant analysis (LDA) to improve the classification of different fire materials. The LDA parameters were applied to a real room dataset to evaluate classification models. Data transformation using LDA improved the classification rate by up to 17% compared to principal component analysis (PCA) approaches used in previous studies.