Over the past few years, building fires have been one of the most common and frequent types of fire, causing severe property losses and fatalities [
1,
2,
3]. Thus, it has become extremely important and imperative to effectively decrease and control the risk of fire in buildings. One of the most critical technologies is early fire warning, and both academia and industry are exploring approaches that enable the prompt detection and accurate determination of scene conditions in the early stages of building fires [
4]. Furthermore, with the development of intelligent buildings, an effective early fire detection and warning system is also an essential prerequisite to provide safe, healthy, and comfortable residential or working conditions.
For achieving early fire detection and warning inside building environments, many algorithms have been presented, which can be mainly divided into three categories, namely optical, smoke, and carbon-monoxide sensors. Li et al. [
5,
6] conducted a fire detection algorithm performance evaluation based on image complexity, which could accurately determine the detection level of the detection algorithm under different image complexity conditions. Li et al. [
7] proposed a new hybrid model for fire prediction based on the visual information of RGB images. The model achieves promising performance, which also shows the potential to monitor the constant changes in a building fire through the continuous processing of images of flames. Xie et al. [
8] employed an early fire detection method for shielded indoor environments based on fire light reflection characteristics and established a multi-expert system by extracting the change characteristics of the fire area with light reflection. They then verified the method based on a large data evaluation system. However, in the above methods, they ignored the early stages of the fire that may not involve the open flame but cannot determine the correct scene conditions in time. The temperature, another major feature of fire, has also attracted significant attention. Ji et al. [
9] proposed a machine-learning-based real-time prediction method for monitoring physical parameters for early fire warnings based on temperatures. The results show that the trained intelligences improve the accuracy and reliability of building fire warnings. Sun et al. [
10] verified a bio-inspired artificial intelligence algorithm driven by temperature data to detect fire in 3D spaces. Yusuf et al. [
3] presented a linearly regressive artificial-neural-network-based technique to predict temperature increases caused by building fire environments. This method predicts temperature ranges in a burning compartment based on the historic fire behavior data modelled via a neural network algorithm. Garrity et al. [
11] exploited a compact embedded artificial neural network (ANN) with a second-stage classifier, reading temperature data from the in-built thermocouple, to produce the output of predicted temperatures. However, this technical approach, namely its ability to judge temperature fluctuations in special environments, still requires further improvement. To compensate for the deficiencies of the feature parameter, Zhang et al. [
12] proposed DBN-R-LSTM-NN to classify and predict fire smoke and other features based on data collected from IoT sensors. Pincoot et al. [
13] designed a vision-based indoor fire and smoke detection system with small training and test datasets, whereby different pixel density images, high-density smoke environments, and flame density scenes were recognized. Qiu et al. [
14] developed a distributed feedback carbon monoxide (CO) sensor based on lasers used for the early warning detection of fire and verified the reliability of this sensor via different experiments. Li et al. [
15] studied and proposed an early fire detection method based on a gas turbulent diffusion (GTD) model and particle swarm optimization (PSO). The experimental results verify that the sensor system has good fire detection and location performance. Chen et al. [
16] proposed a fast and cost-effective indoor fire alarm system, which is used to find the fire, carbon monoxide, smoke, temperature, and humidity data in real time and conduct effective data analysis and classification. For smoke detection, the influence of smoke and dust in the environment represents a challenge.
Although vision-based sensing systems have shown good performance, massive and complex datasets often delay the time in the decision-making process [
17]. Thus, visual-based sensing systems are not appropriate for early fire detection where decision making needs to be conducted in limited time. Additionally, vision-based sensing systems are inappropriate inside buildings because of the size and cost of devices, as well as privacy concerns that make them unsuitable for installation in multiple rooms and private homes. Meanwhile, single chemical sensor fire detectors are susceptible to interference from environmental conditions such as electromagnetic waves, water vapor, dust, cigarette smoke, and cooking smoke [
18], which means that most smoke detectors will have false alarms when they detect these interfering conditions. Similarly, single temperature sensor devices still suffer from the misjudgment of environmental temperature fluctuations. They cannot effectively distinguish between early fire signals and environmental interference signals and cannot properly send early fire warning signals.
Due to the rapid development of machine learning, many intelligent algorithms have been presented to fuse several fire feature parameters [
19,
20,
21,
22]. This method overcomes the singularity and instability of the traditional threshold judgment method, which can significantly improve the accuracy of fire detection. Therefore, this paper proposes a hybrid feature fusion-based early fire detection method. First, by means of the distributed wireless sensing network, a hybrid feature collection system is developed to collect the smoke concentration, temperature, and CO concentration. On this account, a hybrid feature fusion algorithm, combining an error back propagation (BP) neural network with a least square support vector machine (LSSVM), is proposed. Then, the genetic algorithm (GA) and particle swarm optimization (PSO) are applied to obtain the optimal parameters in the data fusion process of the two algorithms, in order to achieve the optimized feature-level fusion results. Furthermore, the two algorithms are treated as subevidence bodies and the output results are adopted as the basic probability assignment of the D-S evidence theory for decision-level fusion to realize highly sensitive and reliable early fire warnings.