GBRUN: A Gradient Search-based Binary Runge Kutta Optimizer for Feature Selection

Abstract

Feature selection (FS) is a pre-processing technique for data dimensionality reduction in machine learning and data mining algorithms. FS technique reduces the number of features and improves the model generalization ability. This study presents a Gradient Search-based Binary Runge Kutta Optimizer (GBRUN) for solving the FS problem of high-dimensional. First, the proposed method converts the continuous Runge Kutta optimizer (RUN) into a binary version through S-, V-, and U-shaped transfer functions. Second, a gradient search method is introduced to improve the exploration capability of the algorithm. Five standard datasets provided by Arizona State University’s Data Mining and Machine Learning Lab were selected to verify the performance of the GBRUN algorithm. The experimental results show that GBRUN has better performance than other advanced algorithms regarding classification accuracy and the number of selected features. Moreover, the GBRUN algorithm is also combined with EfficientNet in this manuscript, using the GBRUN algorithm to select the features extracted by EfficientNet. The results show that the V-shaped (GBRUN-V) and U-shaped (GBRUN-U) algorithms have better performance than other algorithms.