Alexandros Tzanetos
Georgios Dounias
ABSTRACT
One of the problems that Machine Learning (ML) algorithms face in classification tasks is the Curse of Dimensionality, which refers to the sensitivity of their performance to the data dimensionality. The solution to this problem is to select the features that are of higher importance for the model produced. To improve the performance of a classifier, various meta-heuristic algorithms have been implemented due to their ability to provide optimal solutions in problems that there are multiple candidate solutions, such as in Feature Selection (FS). In this study, Sonar Inspired Optimization (SIO) algorithm is used to perform FS in order to improve the performance of a state-of-the-art classifier, i.e. k-NN. SIO’s performance is compared with other nature-inspired meta-heuristic algorithms that have been used for the same task.
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