Binary butterfly optimization approach (bBOA) is a recent high performing feature selection algorithm presented in 2018 which is based on the food foraging behavior of butterflies. This paper tries to improve the structure of the bBOA to enhance its classification accuracy, dimension reduction and reliability in feature selection task for who are interested in the fields of data mining and pattern recognition. The new initialization strategy and differential evolution strategy are applied to reduce the randomness of bBOA’s initialization and local search process. Then, a new parameter is added to make the bBOA’s transfer function more adaptive to the change of exploration and exploitation. Besides, evolution population dynamics (EPD) mechanism is employed as an extension of bBOA. The new method called optimization and extension of binary butterfly optimization approaches (OEbBOA) is tested with the K nearest neighbor classier in which twenty UCI datasets and seven recent algorithms are utilized to assess the performance of the OEbBOA algorithm. The experimental results and nonparametric Wilcoxons rank sum test confirm the efficiency of the proposed OEbBOA in maximizing classification accuracy while minimizing the number of features selected.

Feature selection is an important method to reduce the number of attributes of high-dimensional data and an essential preprocess work in classification. It eliminates irrelevant, redundant and noisy features, improves the performance of the model and reduces the computational burden. Fruit fly optimization algorithm is a new algorithm proposed in recent years, which imitates the foraging behavior of fruit fly. To our best knowledge, it has not been systematically applied to feature selection. This paper uses the fruit fly optimization algorithm as a search strategy and designs a wrapper-based feature selection method, named BIFFOA (binary improved fruit fly optimization algorithm). Besides, four different strategies based on EPD (evolutionary population dynamics) and new mutation operators are employed to enhance BIFFOA. Extensive experiments on 25 datasets (see Table t1) show that the performance of BIFFOA is better than several state-of-the-art algorithms.