###### Abstract
FS can be deployed in many fields, such as image processing/computer vision [11] (e.g., image classification), text mining [12] (e.g., text clustering and classification), industrial applications [13; 14; 15] (e.g., fault diagnosis and life expectancy), bioinformatics [16] (e.g., biomarker discovery), biomedical engineering, electrical and electronic engineering, computer engineering [17], and detection systems like intrusion detection systems [18; 19] and fraud detection systems [20]. Most FS approaches fall into three main categories: filter, wrapper, and embedded approaches [21]. Filter approaches assess features from the dataset without intervention from classification algorithms, using statistical functions to rank each feature. Wrapper approaches select the best feature subset with a learner intervention, evaluating subsets and marking the best ones [22]. Embedded approaches optimize the learner's objective function, integrating feature selection and the learner into a single process [16; 23]. For example, wrapper FS methods use genetic algorithms to search the feature space and classify data [24]. These approaches calculate feature importance and select inputs that highly affect the classification algorithm [25]. FS algorithms, typically executed by domain experts, aim to select variables of an instance (object) to describe and determine the output class of a given problem [26]. However,  
Figure 1: An illustrative example of the difference between FE and FS [10]. FE infers new features from old ones to represent the samples (see different shapes), and the number of these new features is not necessarily lower than that of the original ones. On the other hand, FS aims to select the most representative features of the original features of the samples (see similar shapes), and the number of features in the selected subset is typically lower than the number of features in the original dataset. the exhaustive or precise search strategies used to assess all potential feature subsets and identify the optimal one are often time- and resource-intensive, making their application impractical [27]. For example, when dealing with a sizable dataset comprising \(d\) features, employing a thorough approach would entail evaluating \(2^{d}\) potential subsets, resulting in performance issues. Recognized as an NP-hard problem, FS has prompted researchers to explore stochastic methods leveraging randomness for resolution [3; 27]. Randomness-based algorithms present a practical and efficient alternative, effectively conserving time and resources. Particularly, population-based algorithms have emerged as viable solutions to mitigate the resource drain associated with FS [3]. While numerous prior studies have delved into FS, they exhibit limitations that hinder their practical application. Firstly, traditional FS approaches frequently grapple with overfitting, particularly within high-dimensional feature spaces [28]. Although these methods may optimize training performance by selecting variables, their generalization to unseen data is often subpar. Secondly, embedded FS techniques relying on biased feature scoring methods during training, such as binary decision trees, may falter during the testing phase [29]. Thirdly, certain FS methodologies entail computationally intensive procedures, rendering them unsuitable for handling large-volume datasets. Notably, forward [30] and backward [31] FS methods exemplify this issue. Moreover, some FS strategies assume independence among variables, a premise that may not hold true in complex real-world scenarios. Additionally, certain FS techniques are susceptible to noise and outlier features, resulting in the selection of suboptimal feature subsets [32]. In this paper, we introduce a new method for feature selection called MBHO (the Modified Black Hole Optimization Algorithm). MBHO combines the black hole algorithm with inversion mutation. The black hole algorithm (BHO) has been used in many studies to solve optimization problems across different areas, showing good results [33; 34; 35].