A V-Shaped Binary Crow Search Algorithm for Feature Selection
Rodrigo Clemente Thom de Souza, Leandro dos Santos Coelho, Camila Andrade de Macedo, Juliano Pierezan · 2018
Feature Selection (FS) is the process of identifying and separating relevant features of a dataset to obtain the best solutions for a pattern-classification or regression problem. The main benefits of FS include more accurate classification models, simplified interpretation of models and a reduction in the processing time required for classification. One of the main approaches used in FS involves wrappers. In this approach features are selected based on an evaluation performed by a classification algorithm. Recently, an optimization bio-inspired metaheuristic based on the intelligent behavior of crows called Crow Search Algorithm (CSA) has been proposed. CSA works based on the idea that crows store their excess food in hiding places and retrieve it when the food is needed. The main reasons of using CSA are its easy implementation, few control parameters to adjust, fast convergence speed and high efficiency. To further enhance the performance of the classical CSA algorithm, this paper proposes a new wrapper based in a “v-shaped” binarization of the CSA. The wrapper, which is referred to here as Binary CSA (BCSA), is applied to six benchmark data sets. The paper compares and discusses the advantages and disadvantages of the proposed technique in terms of classification accuracy, number of selected features and computational cost against some classical and state-of-art algorithms. The results were encouraging and showed that BCSA achieved very good results in terms of classification accuracy and also selected subsets with a small number of features with a relatively low computational cost.