Zgadzam się
Nasza strona zapisuje niewielkie pliki tekstowe, nazywane ciasteczkami (ang. cookies) na Twoim urządzeniu w celu lepszego dostosowania treści oraz dla celów statystycznych. Możesz wyłączyć możliwość ich zapisu, zmieniając ustawienia Twojej przeglądarki. Korzystanie z naszej strony bez zmiany ustawień oznacza zgodę na przechowywanie cookies w Twoim urządzeniu.
Feature selection is the procedure of extracting the optimal subset of features from an
elementary feature set, to reduce the dimensionality of the data. It is an important part of improving
the classification accuracy of classification algorithms for big data. Hybrid metaheuristics is one of
the most popular methods for dealing with optimization issues. This article proposes a novel feature
selection technique called MetaSCA, derived from the standard sine cosine algorithm (SCA).
Founded on the SCA, the golden sine section coefficient is added, to diminish the search area for
feature selection. In addition, a multi‐level adjustment factor strategy is adopted to obtain an
equilibrium between exploration and exploitation. The performance of MetaSCA was assessed
using the following evaluation indicators: average fitness, worst fitness, optimal fitness,
classification accuracy, average proportion of optimal feature subsets, feature selection time, and
standard deviation. The performance was measured on the UCI data set and then compared with
three algorithms: the sine cosine algorithm (SCA), particle swarm optimization (PSO), and whale
optimization algorithm (WOA). It was demonstrated by the simulation data results that the
MetaSCA technique had the best accuracy and optimal feature subset in feature selection on the
UCI data sets, in most of the cases.