## 1 Introduction  
With the advancement of bioinformatic technology in the domain of animal breeding, biological datasets, especially those derived from microarrays, have become vital analytical tools for animal scientists and breeders. These datasets significantly improve the precision of trait selection and breeding outcomes, enhancing decision-making processes in breeding programs [1]. However, the analysis of microarray datasets faces significant challenges due to the curse of dimensionality and the complexity of feature interactions [2]. Microarray datasets are typically characterized by a limited number of samples yet a vast array of gene features, many of which are redundant or irrelevant. This redundancy and irrelevance can obscure vital features, leading to poor performance of machine learning algorithms [3]. Additionally, an excessive number of features can lead to the curse of dimensionality, an issue characterized by an exponential increase in problem complexity as the number of features grows. This complication can hinder pattern detection and may result in overfitting or poor model generalization. To solve these issues, feature selection (FS) is employed as a critical preprocessing step, aiming to optimize the feature space by removing irrelevant and redundant features [4]. This process not only reduces the dimensionality of datasets but also enhances the accuracy, interpretability, and efficiency of the machine learning models [5, 6]. Importantly, FS assists in uncovering the crucial mechanisms that link gene expression with specific traits in livestock, supports the analysis of data patterns to identify novel genetic markers correlated with specific traits, and contributes to minimizing expenses in breeding programs [7]. This enhancement in dataset management and analysis improves the efficacy of machine learning algorithms, leading to more accurate associations between traits, genes, and their expression levels, thereby facilitating more informed decisions in trait selection and breeding strategies [8, 9]. Over the past two decades, FS has been extensively studied and has had a significant impact in many fields [10; 11]. There are three main types of FS methods: filter methods, wrapper methods, and embedded methods. Filter methods are based on the measurement of data characteristics that are independent of any machine learning (ML) algorithms. This method evaluates the relevance between features and the target class (as well as the dependence between features) by using different measurement criteria, such as information, distance, dependence, and consistency, and then selects features based on the evaluation results. In contrast, wrapper and embedded methods select features using a predetermined ML algorithm. Wrapper methods select features based on the classification accuracy of the ML algorithm, while embedded methods select features based on the contribution to the learning process of the ML algorithm. However, compared to filter methods, wrapper and embedded methods have several significant drawbacks, especially for high-dimensional datasets. First, they tend to inherit the bias of the predetermined ML algorithm, resulting in poor versatility for other ML algorithms. Second, they focus only on the classification accuracy of the selected features, which may lead to overfitting or the elimination of some potentially important features. Third and most importantly, they are more computationally intensive and time-consuming because they involve iterative processes of feature search, ML model building, and testing. Therefore, the focus of this study is on filter methods, which can provide a more efficient and versatile approach to FS without being tied to specific ML algorithms. Early filter methods evaluate the relevance of each individual feature with the target class and then remove the irrelevant and redundant features according to their rankings of relevance. Such methods are called individual evaluation methods (IEMs) [12; 13].