MPA + SVM: An Active Feature Selection Approach in High-Dimensional Data Sets

Picking a subset of possible features is a vital step in the data-mining procedure. The decisive goal of feature-selection is to determine the optimal number of superior characteristics to make best use of the presentation of the learning algorithm. However, this problem becomes increasingly challenging to resolve as the number of features in a data set increase. Therefore, to identify the optimal feature combinations, contemporary optimization techniques are used. Numerous optimization problems have been successfully resolved using the innovative metaheuristic known as the marine predators algorithm (MPA). Support vector machines (SVMs) are a crucial technique that is expertly applied to classification issues. In this work, the issue of feature picking in large dimensional data sets is solved by adjusting the MPA using the SVM as a classifier. In order to address the problem of feature selection in large dimensional data sets, the current study suggests MPA + SVM. Ten high-dimensional data sets got from the Arizona State University (ASU) source were employed to prove the usefulness of the planned method; the outcomes are likened with those of the additional six cutting-edge picking features algorithms. We compared the following algorithms: atom search optimization (ASO), satin bowerbird optimizer (SBO), emperor penguin optimizer (EPO), equilibrium optimizer (EO), monarch butterfly optimization (MBO), and sine cosine algorithm (SCA). The consequences confirm that the planned MPA+SVM approach outperformed several metaheuristic algorithms and presented a remarkable capability to pick the utmost weighty and optimum features. MPA + SVM yields the lowermost averaged error rates, minimal classification standard deviation (STD) values, and FS rates across all data sets.