Features Selection (FS) techniques have been applied to several real-world applications which contain high dimension data. These FS techniques have main objectives that aim to achieve them, such as removing irrelevant features and increasing classification accuracy. This is considered a bi-objectives optimization problem that requires a suitable technique that can balance between the objectives. So, different sets of FS techniques have been developed, and those techniques that depend on meta-heuristic (MH) established their performance overall traditional FS techniques. However, these MH approaches still require more enhancement to neutralize their exploration and exploitation abilities during the searching process. Enhancing the meta-heuristic optimization algorithm using the perspective of fractional calculus (FC) is an attractive and novel approach. In this paper, the slime mould algorithm (SMA) is modified using the FC for handling the optimizer drawback of the inefficient diversification phase. As a result, a fractional-order SMA is proposed to avoid the local solutions and discover the search landscape efficiently via considering a historic memorize of agents’ positions. The proposed FOSMA is applied to extract features from a set of real-world data and increase classification accuracy. For boosting the optimizer performance while processing with these datasets, the rough set (RS) is used as the fitness function to handle the uncertainty inside the real-world data. Finally, the proposed FOSMA’s results are compared with a set of well-known FS techniques to investigate its performance. The comparison illustrates the superiority of FOSMA in providing high accuracy.