# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 2 Related Works  
Recently, meta-heuristic algorithms have attracted the attention of many scholars. These algorithms can be used to solve many real engineering tasks, such as path planning [14; 15; 16], feature selection [17; 18; 19], function optimization [20; 21; 22], and the traveling salesman problem [23; 24; 25]. Although various meta-heuristics have been developed to deal with FS over the years, the significant increase in data dimensionality brings great challenges; therefore, it is worth continuing looking for effective strategies to make meta-heuristic algorithms perform better for high-dimensional FS problems [26].  
ABC was proposed in 2005 by [PERSON] group to optimize algebraic problems [27]. Single-objective ABC was first used to address the FS problem in 2012 [18; 28]. Almost all meta-heuristic algorithms have the problem of an imbalance between exploration and exploitation [29], and the ABC algorithm is no exception. There are a lot of studies on the ABC algorithm, seeking to improve its exploitation capability. To accelerate the convergence speed of the ABC algorithm, [PERSON] et al. [30] proposed the KnABC algorithm, which introduced Knee Points into the employed bee phase and onlooker bee phase. The results show that this algorithm has a significant effect on reducing the number of features and increasing the classification accuracy. [PERSON] et al. [31] utilized the ACO algorithm for colony initialization, and took the initialization results as the food sources of the ABC algorithm for further optimization so as to integrate the ACO and ABC algorithms; the resulting algorithm's performance was better than that of ABC or ACO alone. [PERSON] et al. [10] proposed two hybrid ABC algorithms, i.e., ABC-PSO and ABC-GA, which integrate the PSO algorithm and GA algorithm into the framework of the original ABC algorithm in different bee phases, respectively. The experimental results showed that ABC-GA obtained better results than some other existing methods. [PERSON] et al. [32] proposed the EABC-FS algorithm, in which the employed bees and onlooker bees made full use of the best solutions in the current swarm to enhance the exploitation ability of the ABC algorithm. The experimental results showed that the performance of the algorithm achieved by introducing such fusion strategies was greatly improved. Moreover, many other studies have shown that the ABC algorithm faces the problem of an insufficient exploitation ability, which results in it becoming trapped in a local optimum and having a low convergence speed [28; 33].  
Although the above-mentioned hybrid variants of the ABC algorithm have achieved promising performance, they do not deeply analyze the exploitation and exploration abilities in different bee phases of the overall framework. Moreover, few of these algorithms have been developed for high-dimensional FS. Therefore, this paper proposes a novel exploration and exploitation trade-off ABC algorithm by modifying the original overall framework, and applies it to high-dimensional datasets. This new framework strengthens the exploitation ability in the onlooker bee phase by using operators with high exploitation capacities. Additionally, the function of scout bees is discussed in detail, and verified by experiments.