# Improved Binary Grey Wolf Optimization Approaches for Feature Selection Optimization
## 1 Introduction  
The huge amount of data in many areas leads to the formation of high-dimensional datasets. Large amounts of data and a massive number of attributes increase computational complexity and decrease the efficiency of data mining [1, 2] and learning processes [3]. The large amount of data also leads to a problem in classification tasks known as the curse of dimensionality [2, 4]. Feature selection (FS) is a crucial process for shrinking the size of adataset by removing duplicated and useless features to enhance classification performance and/or reduce computational costs [2]. The FS process consists of four steps, as shown in Figure 1[5]. The four steps are subset generation, subset evaluation, verification for a stopping criterion, and result validation. Subset generation is the process of searching for feasible solutions to generate candidate feature subsets for evaluation. Subset evaluation is then used to evaluate the newly generated subset by comparing it with a previous best one in terms of a certain evaluation criterion. The new subset may replace the previous best subset if the former is found to be better than the latter. The stopping criterion determines whether the feature selection process must be stopped or not due to, for example, achieving a certain minimum number of features or reaching a certain maximum number of iterations. Finally, result validation is used to measure the accuracy of the result. It is responsible for evaluating the FS approach by validating the selected subset of features using a testing dataset. The search algorithms that are commonly used to search for the best representative features in a dataset can be categorized into three different classes, namely, exhaustive, random, and heuristic search techniques [2; 6]. An exhaustive search technique is time-consuming and, consequently, exhibits high costs since it investigates all possible solutions [2; 7]. A random search technique generates a random subset of features from the dataset [8]. A heuristic search technique is faster than the former two searching techniques since it allows the user to obtain the near-optimal subset by applying some heuristic behavior without having to check all possible feature subsets [9]. To evaluate the generated feature subsets, filter, wrapper, and hybrid approaches can be used [10]. The filter approach evaluates a new subset of features based on a certain ranking method to return the most important attributes without applying any learning algorithm [11]. The wrapper approach evaluates the chosen features based on the feedback from a learning algorithm which takes, for example, classification accuracy into account [12]. The hybrid approach benefits from the advantages of both the filter and wrapper methods [13]. Metaheuristics are frequently used in the literature to solve optimization problems, specifically with broad searching areas to find the best solution in a reasonable time [14]. This paper focuses on metaheuristic search strategies to deal with the FS optimization problem. Swarm intelligence (SI) algorithms are metaheuristic methods that have been broadly used to resolve a variety of optimization issues [10]. These algorithms imitate the social attitudes of animals, insects, or birds when seeking food. SI suffers from falling into the local optima, especially when searching in a high-dimensional search space. Such algorithms require a balance between global search, i.e., exploration, and local search, i.e., exploitation, to discover promising areas in the search space and find a global optimum. The focus of this paper is to provide a heuristic solution to achieve this balance. In this paper, three hybrid FS optimization approaches are proposed. The proposed approaches combine the Grey Wolf Optimization (GWO) [15; 16] and Particle Swarm Op  
Figure 1: Feature selection process [5]. timization (PSO) [17; 18] algorithms by applying GWO followed by PSO, and then each approach manipulates the solutions obtained by both algorithms in a different way.