## 1 Introduction  
With society gradually becoming digitalized, the question of how to extract useful information effectively from complex and huge data has become the focus of research in recent years. Machine learning is one of the important research fields of information recognition and pattern recognition in data sets [1]. It has been widely used due to its strong data processing ability. Machine learning is evolving from computer science [2], which is a multidisciplinary and interdisciplinary major. It covers probability theory, statistics, approximate theory, and complex algorithms, and it can design efficient and accurate prediction algorithms [3]. It can be divided into supervised learning and unsupervised learning according to the learning mode [4]. SVM is one of the most widely used supervised learning algorithms. [PERSON] and other researchers built the cornerstone of SVM [5]. It is widely used in pattern recognition [6; 7], text recognition [8], biomedical [9; 10; 11], imaging medicine [12], anomaly detection [13] and in other fields. SVM classifies feature spaces into two categories and defines a non-parametric method for finding decision boundaries [14]. SVM hasshown excellent performance in many problems [15]. Using the support vector machines to solve problems can improve generalization performance, improve computational efficiency, reduce running time, and produce a very accurate classification model [16; 17]. The Support Vector Machines can solve both linear and nonlinear classification problems. When the data set is linearly inseparable, the nonlinear problem is usually transformed into a linear problem. Then, the data set can be used to construct an optimal hyperplane in the feature space, so that the feature space can be divided into two classes more easily [18]. However, when machine learning processes high-dimensional data sets, there will be problems such as noise and redundant data [19]. In this case, feature selection [20] can be used to reduce the number of features. Feature selection is to select the optimal feature subset from the original data set [21]. According to the form of feature selection, the feature selection methods can be divided into the following three types: filter, wrapper, and embedded [22; 23; 24]. The wrapper-based approach is used in this article. The wrapper method, which relies on the classification method [25], is more efficient than the other two, but its computational intensity is relatively greater [26]. The filter method [27] uses numerous indicators to evaluate and select high-order features according to their discriminant attributes [28]. After that it will select the subset with the richest information [29]. The main idea of the wrapper approach is to treat the selection of subsets as a search optimization problem. At first it will generate different combinations, then evaluate them, and finally compare them with other combinations. The wrapper method is often used in feature selection problems due to the superiority of its calculation results, and the wrapper method can directly interact with classifier [30]. The wrapper method can be effectively combined with the meta-heuristic optimization algorithm so to achieve better practical application effect. In recent years, many researchers have combined optimization algorithms with SVM to solve problems. Influenced by this, this paper tries to combine ChOA with SVM for feature selection and parameter optimization. ChOA is a new meta-heuristic optimization algorithm proposed by [PERSON] and [PERSON] in 2019 [31] which is inspired by the hierarchy mechanism of chimps in nature when they are hunting. It can solve the problem of slow convergence and local optimum when it is solving the serious dimensional problems. However, the ChOA still has some shortcomings. Firstly, the population diversity of the algorithm is insufficient in the initial stage. Secondly, there is a risk of falling into local optimal in the final search stage. Therefore, an Enhanced Chimp Optimization Algorithm is proposed in this paper.