###### Abstract  
High-dimensional datasets often harbor redundant, irrelevant, and noisy features that detrimentally impact classification algorithm performance. Feature selection (FS) aims to mitigate this issue by identifying and retaining only the most pertinent features, thus reducing dataset dimensions. In this study, we propose an FS approach based on black hole algorithms (BHOs) augmented with a mutation technique termed MBHO. BHO typically comprises two primary phases. During the exploration phase, a set of stars is iteratively modified based on existing solutions, with the best star selected as the \"black hole\". In the exploration phase, stars nearing the event horizon are replaced, preventing the algorithm from being trapped in local optima. To address the potential randomness-induced challenges, we introduce inversion mutation. Moreover, we enhance a widely used objective function for wrapper feature selection by integrating two new terms based on the correlation among selected features and between features and classification labels. Additionally, we employ a transfer function, the V2 transfer function, to convert continuous values into discrete ones, thereby enhancing the search process. Our approach undergoes rigorous evaluation experiments using fourteen benchmark datasets, and it is compared favorably against Binary Cuckoo Search (BCS), Mutual Information Maximization (MIM), Joint Mutual Information (JMI), and minimum Redundancy Maximum Relevance (mRMR), approaches. The results demonstrate the efficacy of our proposed model in selecting superior features that enhance classifier performance metrics. Thus, MBHO is presented as a viable alternative to the existing state-of-the-art approaches. We make our implementation source code available for community use and further development. feature selection; wrapper feature selection; black hole algorithm; cuckoo search algorithm; correlation functions; transfer function; data mining 2020 April 2024 2020 31 May 2020 12 June  
subset of features, which is called \"minimal optimal\", via selecting the smallest possible subset of features that achieve the best classification result is essential [3; 4; 5]. This process of feature selection (also known as dimensionality reduction) is used in preprocessing steps to enhance the performance of ML models. Dimensionality reduction can be divided into three methods: feature construction (FC), feature extraction (FE), also called feature projection, and feature selection (FS). FC is a process of increasing the expressive power of the original features by generating additional features, thereby revealing the relationships between the features and augmenting the space of the features. In other words, FC aims to transform the original representation space of features into new spaces that improve data mining objectives. It is important to note that, in FC, it is not necessary that all constructed features are useful [6; 7]. On the other hand, FE is a process of generating new significant features from the original ones by performing transformation functions while ensuring that the number of produced features is lower than that of the original ones. Meanwhile, FS is a process of selecting the best possible subset of features from the original space of features in order to improve the performance of the classifier [8]. In other words, FS is a technique used to select the best subset of features that aims to reduce the size of the dataset structure without significantly decreasing the performance of data mining or classification algorithms [3]. Figure 1 shows the difference between feature selection and feature extraction. The optimal subset of features provides faster and more cost-effective predictors with a better analysis of the selected features instead of high-dimensional, complex datasets that contain a lot of variables [9]. FS is a very useful technique for various studies that tend to work with large data and complex problems.