Abstract

Technological advances and the digitization of information have allowed us to obtain a large amount of data from different processes such as medicine, commerce, and mining, among others. All this data has been used as input by different researchers in machine learning techniques to speed up the decision making process of professionals. Machine learning techniques are susceptible to data, so cleaning it to remove irrelevant and redundant information is necessary. This removal of information is known as the Feature Selection Problem. This work presents the Pendulum Search Algorithm (PSA) applied to solve the Feature Selection Problem. As the PSA is a metaheuristic designed for continuous optimization problems, a binarization process is performed using the Two-Step Technique. In particular, we have used four transfer functions and three binarization rules to observe their impact when binarizing PSA. Preliminary results indicate that the V-Shaped transfer function and the Standard binarization rule is the binarization scheme that obtains the best experimental results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.