Binary Golden Eagle Optimizer with Time-Varying Flight Length for feature selection

摘要

The concept of any method to resolve feature selection issues is to identify a subset of the original features. However, determining a minimal feature subset is considered an NP-hard problem. Many existing feature selection methods, namely particle swarm optimization (PSO), grey wolf optimization (GWO) etc., are complex and consume more time. This article proposes a new wrapper-based method that employs Binary Golden Eagle Optimizer with Time Varying Flight Length (BGEO-TVFL) to reduce the feature selection issues. The proposed method provides optimal feature selection based on Golden Eagle Optimizer (GEO). GEO is a recently presented swarm-based meta-heuristics algorithm that imitates the smartness of Golden Eagles (GEs). GEO algorithm performs on continuous space; however, the feature selection is in discrete space. Hence, the continuous space has transformed into a discrete space by employing transfer functions. Here, eight different transfer functions have been applied to determine the best transfer function and test the BGEO-TVFL. Moreover, a time-varying flight length (TVFL) has been employed to balance the exploration and exploitation in GEO. Finally, the performance of the proposed BGEO-TVFL has been determined by focusing on various metrics under different UC Irvine (UCI) datasets. The best variant has been selected and analyzed the performance with existing standard filter feature selection approaches and wrapper based feature selection methods. The existing methods include Bat Algorithm (BAT), Ant Colony Optimization (ACO) algorithm, PSO, GWO, Genetic Algorithm (GA), Cuckoo Search (CS), Golden Eagle Optimizer (GEO), ReliefF, Information Gain (IG), Correlation-based feature selection (CFS), and Gain Ratio (GR) respectively. In addition, the performance of BGEO-TVFL is also compared and analyzed with state-of-art methods to determine its superiority. Furthermore, the proposed BGEO-TVFL is analyzed with existing algorithms by employing 30 functions from CEC’2017, CEC’2018 and CEC’2020 benchmarks. As a result, the proposed BGEO-TVFL has achieved a better result than existing methods and is suitable for dealing with dimensionality reduction issues.