# Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree
## 1 Introduction  
In the development of electronic technology and artificial intelligence, sensing applications and machine learning algorithms are becoming increasingly intelligent, which promotes the continuous development of machine olfaction. This introduction will briefly place this study in a broad context and highlight why it is important. Machine olfaction is a new type of biomimetic detection technology, which can be used to simulate the working mechanism of biological olfaction. It is often used in the analysis and detection of various gases, such as in pollution control [1; 2], medical technology [3; 4], and oil exploration [5]. Researchers have achieved good results in the study of machine olfaction [6; 7; 8]. In the area of this research, the detection of dangerous flammable and explosive gases is particularly important, and the safety problems caused by gas leakage are prevalent all over the world, which seriously endangers human life [9]. If these gases can be detected, classified and identified timeously and the leakage situation and trend can be determined, the occurrence of dangerous accidents can be avoided to a large extent. Therefore, the detection of flammable and explosive toxic gases is of great significance. The main aspects of machine olfaction include the gas sensor array, data processing and artificial intelligence algorithms as shown in the overall schematic diagram shown in Figure 1[10]. The overalloperational process of the system is to first obtain the gas information through the sensor array; the multi-dimensional data represented by the array can fully characterize the gas type information. After the data are stored and classified, feature extraction and selection are carried out through data analysis and feature engineering, and the processed data of the feature engineering is used by an algorithm to identify the gas. Among them, the artificial intelligence algorithms have an extremely important role, and their accuracy, time efficiency and anti-interference ability all affect the decision-making result of the whole system [11; 12; 13]. In [14], researchers consider that only by adopting the optimal data processing algorithm can the performance of the whole model for machine olfaction be improved. Studies [15] proposed that reasonable improvement of the algorithm is an important support for the development of the current machine olfaction systems. Based on an experimentally acquired metal oxide semiconductor (MOS) gas sensor array dataset, researchers focus on a machine learning classification algorithm to improve the detection accuracy of mixed gas [16; 17; 18]. In the current field of mixed gas detection, many researchers have applied algorithms to achieve good classification results, such as support vector machine (SVM), artificial neural network (ANN), K-nearest neighbour (KNN), etc. [19; 20; 21; 22; 23; 24; 25]. To improve the classification accuracy, Sun proposed an optimized AdaBoost algorithm [26]. The M2 model combined multiple classifiers and performed multiple classification experiments using different fusion rules. The final highest recognition accuracy obtained was 91.75%. In [27], the posterior probability estimation algorithm extracted from an SVM was used to detect 10 kinds of bacterial components in the human blood by machine olfaction, and the recognition accuracy was high, but the time cost was large. In [28], the probabilistic Bayesian algorithm was used to solve the uncertainty relationships in gas source localization, and the Markov decision process path planning algorithm was used to improve the gas localization efficiency. In [29], the artificial neural network (ANN) algorithms improved the resolution of the moisture content detection in soil, but the ANN algorithm lacks explanation. At present, few researchers can improve the time efficiency of the algorithm under the condition of high detection accuracy [30]. Moreover, few researchers consider the accuracy of the gas sensor itself.