## 2 Related Works
### Swarm Intelligence-Based Feature Selection  
There are many swarm intelligence algorithms have been adopted or proposed as the learning algorithm in wrapper-based feature selection methods, and we review some key algorithms as follows.  
[PERSON] et al. [29] proposed an improved binary GWO (IBGWO) algorithm for solving feature selection problems, in which an enhanced opposition-based learning (E-OBL) initialization and a local search strategy were proposed for improving the performance of the algorithm. [PERSON] et al. [30] presented four different improved versions of the sine cosine algorithm (SCA), where the updating mechanism of SCA is the improvements and innovations. [PERSON] et al. [31] proposed a hybrid feature selection approach based on the ReliefF filter method and equilibrium optimizer (EO), which is composed of two phases and tested in some open datasets. [PERSON] et al. [14] proposed three variants of BGWO in addition to the standard variant, applying different transfer functions to tackle the feature selection problem. In [32], two different wrapper feature selection approaches were proposed based on farmland fertility algorithm (FFA), which denoted as BFFAS and BFFAG, and these methods are effective in solving feature selection problems. On the other hand, BA and some variants have been adopted for solving feature selection problems. [PERSON] et al. [33] proposed a bat optimization algorithm for wrapper-based feature selection and conducted simulations based on the CICInvesAndMal2019 benchmark dataset. [PERSON] et al. [34] proposed a feature selection method to identify the relevant subset of features for the machine-learning task using the wrapper approach via BA. [PERSON] et al. [35] presented a wrapper feature selection approach based on bat algorithm (BA) and optimum-path forest (OPF). In [36], the authors proposed an improved BPSO algorithm as an essential tool of pre-processing for solving classification problem, in which a new updating mechanism for calculating Pbest and Gbest were proposed. Moreover, the authors in [37] proposed a binary DA (BDA) and use it to solve the feature selection problems. Likewise, [PERSON] et al. [38] proposed a binary version of the bat algorithm, i.e., BBA, and evaluate its performance in solving the feature selection problems. In addition, in [39], a new hybrid feature selection method was proposed by using the sine cosine algorithm (SCA) and genetic algorithm (GA), and the algorithm is used for solving feature selection problems. Furthermore, [PERSON] et al. [40] proposed a feature selection method via binary gravitational search algorithms (BGSA) in medical datasets, in which they can reduce the number of features by an average of 66% and enhance the accuracy of prediction.  
The aforementioned methods can solve feature selection problems in various applications. However, according to NFL theory, different swarm intelligence algorithms may have different performances in various applications. Therefore, the existing methods are insufficient to solve all feature selection problems, which motivates us to propose an EBBA to handle more feature selection problems in this work.