Feature selection (FS) has the largest infuence on the performance of machine learning methods. FS can remove the irrelevant and redundancy features from the data while preserving the same quality of increasing it. However, the traditional FS methods are time-consuming and can be stuck in local optima. So, the metaheuristic (MH) techniques are used to avoid these limitations since they have several operators that explore and exploit the search domain better than traditional methods. Besides these behaviors of MH, we present an improved atomic orbital search (IAOS) algorithm using a global search strategy that uses the operators of arithmetic optimization algorithm (AOA), which has proven a good exploration ability to provide a promising candidate solution. The opposite-based learning (OBL) is applied to enhance the initial population, which leads to enhancing the convergence rate towards the optimal solution. In addition, a dynamic photon rate is used to enhance the balance between exploration and exploitation. Finally, the sequential backward selection (SBS) is used as a local search strategy to improve the best solution, and this leads to obtaining a set of relevant features that increase the classifcation accuracy. To evaluate the performance of the presented IAOS-SBS as an FS method, a set of twenty UCI datasets is used; also, it is compared with other well-known FS methods. The results show the superiority of IAOS-SBS among the performance measures. Finally, it is concluded that IAOS-SBS can select fewer features with achieving high classifcation accuracy for most of the datasets utilized in the study. This indicates the use of OBL and SBS leads to enhancing the original AOS.