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, mining, among others. All this data has been used by different researchers in machine learning techniques to accelerate the decision making process of professionals. Machine learning techniques are very sensitive to data, so it is necessary to perform a cleaning to remove irrelevant and redundant information. This information removal is known as the feature selection problem. This paper presents the Pendulum Search Algorithm applied to solve the feature selection problem. Since the Pendulum Search Algorithm is a metaheuristic designed for continuous optimization problems, a binarization process is performed using the two-step technique. Preliminary results indicate that our proposal obtains competitive results compared to other metaheuristics extracted from the literature that solve well-known benchmarks.