## 2 Related Work  
Feature selection encompasses various classification methods, including Filter, Wrapper, Embedded, and Hybrid, depending on the combination of feature selection techniques and learners [11]. This section primarily focuses on introducing these methods. One example of a filtering feature selection algorithm is the filter feature selection algorithm framework based on feature ranking. This method employs specific evaluation criteria to assign scores to each feature, ranks the features in descending order based on their scores, and selects the top-k features. In this study, a popular filter feature selection algorithm based on the chi-square test [12] is employed. The chi-square test is a hypothesis test that approximates the distribution of statistics to the chi-square distribution under the null hypothesis. Its fundamental concept involves assessing the correlation between two variables based on sample rates. Consequently, a larger chi-square value indicates a stronger relationship between the two classification variables and a lower degree of independence. Conversely, a smaller value suggests a weaker relationship and a higher degree of independence. The filter feature selection algorithm employed in this study demonstrates high efficiency, enabling rapid removal of a significant number of irrelevant features when dealing with high-dimensional data. However, it is not guaranteed that the combination of all strongly correlated features will yield satisfactory overall performance for the feature subset. Numerous features exhibit high redundancy and this redundancy detrimentally affects the overall performance of the feature subset. Additionally, some weakly correlated features may be important and essential. The embedded feature selection algorithm is to embed the classification algorithm into the learning process. When the training process of the classification algorithm is finished, the feature subset can be obtained. The embedded feature selection method can solve the problem of high redundancy of filter algorithm results based on feature sorting and can also solve the problem of high time complexity of the wrapper algorithm. The embedded feature selection algorithm does not have a unified process framework and different algorithm frameworks are different. The boosting algorithm is widely recognized as an effective ensemble learning technique in the field of data mining. By assigning weights and combining individual weak classifiers, it effectively reduces errors, and yields a higher accuracy and more precise classification results [13]. The fundamental concept behind boosting is to iteratively diminish the residuals and further minimize residuals based on the gradient direction of the previous model, resulting in the creation of a new model [13]. [PERSON] et al. [14] proposed XGBoost, also known as extreme gradient boosting, in 2015. The XGBoost algorithm draws inspiration from random forest during the training process by sampling the data at each iteration and utilizing certain sample characteristics for training purposes [15]. Nevertheless, the number of embedded algorithms is limited and the performance is contingent upon the characteristics of the learners. Certain learners possess inherent feature selection capabilities, while others lack this functionality. Furthermore, while the feature subset exhibits exceptional performance, it is prone to overfitting as it is specifically optimized for itself. The hybrid feature selection algorithm draws inspiration from ensemble learning techniques. This approach trains multiple feature selection methods and combines their results, leading to improved performance compared to using a single method. One mainstream hybrid feature selection algorithm is the use of random forest as the learner [16]. In this study, recursive feature selection with random forest importance is employed.