# Feature Selection for High-Dimensional Datasets through a Novel Artificial Bee Colony Framework
## 1 Introduction  
Due to the rapid development of data acquisition technology, a great deal of digital information is becoming more easily collected and included in datasets. However, not all features in datasets are useful for a target problem. In other words, there are many redundant and irrelevant features in high-dimensional datasets, so feature selection (FS) is used as a vital data preprocessing step in data mining and machine learning [1]. However, FS is an NP-hard problem. For an n-dimensional dataset, there are 2n feature subsets, which is difficult to solve with an exhaustive method. With a good FS method, we can not only get higher classification accuracy, but also reduce the complexity of calculation. In order to improve the search efficiency of FS algorithms, many scholars propose algorithms, which can be roughly divided into three types: filter method, wrapper method and embedded method [2]. Among them, the wrapper method is widely used because of its good classification ability. Therefore, this paper studies the wrapper FS method. The wrapper approach mainly consists of three parts: classifiers, feature subset evaluation criteria and search techniques [3]. Among them, an effective search technique is crucial for the performance of FS algorithms. It is worth mentioning that meta-heuristic methods, such as the artificial bee colony (ABC) algorithm [4], the particle swarm optimization (PSO) algorithm [5], the differential evolution (DE) algorithm [6], the grey wolf optimization (GWO) algorithm [7], the whale optimization algorithm (WOA) [8], and manyother algorithms [9] have provided good search strategies for the FS task. Unlike the exact search mechanisms, meta-heuristic methods exhibit superior performance, as they do not generate all possible solutions for a given task. Meta-heuristic algorithms have exploration and exploitation abilities, and the trade-off between both abilities is very important for the performance of these algorithms. The exploration acts to discover various unknown regions for more potential solutions, while the exploitation attempts to generate better solutions on the basis of the information provided by existing solutions. In some meta-heuristic search techniques, the ability of exploration is stronger, while in others, the exploitation performs better [10; 11]. Exploring the search region and exploiting the best solution are two contradictory criteria that must be considered simultaneously when designing a good meta-heuristic algorithm. The key to improving an algorithm is to achieve a good balance between exploration and exploitation [3; 12]. The ABC algorithm is an optimization algorithm that is inspired by the foraging behavior of a honey bee swarm. ABC has been successfully applied to various optimization problems due to its good properties, such as few parameters to control, its high flexibility, and its strong global search ability [11]. However, ABC converges slowly because of the absence of a strong local exploitation ability [10; 13]. From the above considerations, we propose a new framework to enhance the exploitation performance of the ABC algorithm, so as to realize a trade-off between the exploration and exploitation capabilities of the FS method, and raise the optimization efficiency and effectiveness. The contributions of this paper are as follows:  
1. In order to trade off the exploitation and exploration abilities of ABC, we use operators with strong exploitation abilities to enhance the exploitation ability in the phase of onlooker bee;
2. This paper analyzes the functional behavior of the scout bee phase and finds that this phase may be redundant while dealing with high-dimensional FS problems, and so eliminating this phase can reduce the computational time of the algorithm;
3. The proposed framework is designed as a general framework that can be used to adapt many ABC variants for the FS problems. The remainder of this paper is illustrated as follows: Section 2 briefly describes the related works of the ABC algorithm.