# Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution
## 1 Introduction
In effect, the limited number of the unsupervised FS methods found in the literature has only been used is unsupervised wrapper FS [14]. In light of what has been discussed above, it can be summarized that using memetic (hybrid) methods could be more successful than using filter or wrapper methods separately. However, many of the available filter methods used with hybrid methods are not suitable for unsupervised FS due to the necessity of the existence of class labels. As an exception, there are a few examples of unsupervised text filter methods such as the Mean Absolute Distance (MAD) method reported in [11]. A feature relevancy score is assigned by the MAD for every feature by calculating the distance of each feature from the mean of the entire set. The Mean-Median (MM) [12] is also another example of an unsupervised filter Unlike the MAD, the MM calculates the absolute distance of each feature between the median and mean values. All these methods are capable of local search [11; 12; 13; 14; 15; 16]. Simulated Annealing (SA), which is a method used to solve optimization problems, simulates the heating of material and then slowly reducing the temperature to decrease the defects, therefore, reduce the system energy. SA can be used for unsupervised feature selection. SA is a metaheuristic method that differs from other methods. It is not a population-based method; instead, it only works on a single solution at a time, and that makes it a good candidate for local search. More recently, a Whale Optimization Algorithm (WOA) was used in combination with the SA in a hybrid wrapper-filter scheme. The WOA was hybridized with SA to improve the quality of the resultant features [17]. To summarize, it is noted that the majority of the research conducted in the last decade recommended the use of the hybrid wrapper and filter methods for FS due to their complementary advantages. Using this hybrid FS scheme is based on the same principles as the memetic optimization explained earlier. In that context, each optimization problem should start with a random number of solutions. In the proposed FS problem, we start the proposed FS-based optimization method with a random number of feature combinations. Memetic optimization is expected to refine that number of solutions and reduce it into an optimal solution. The reason behind using memetic optimization to conduct our proposed feature selection method is that it has the capability of combining the local with the global search to get better search ability for the best solution. In FS, the local search represents the filter method, while the global search represents the wrapper. Thus, it becomes important in our method to use memetic optimization to do this combination [18]. This paper proposes the Dichotomous Differential Evolution with Simulated Annealing (DDESA) feature selection methods. SA is used to improve the exploitation of solution space, and dichotomous DE is used as a global search to perform the explorative aspect. SA could be used for a supervised and unsupervised feature selection. In this presented work, SA is utilized as a local search unsupervised filter method, which is an essential component in memetic optimization. The Mean Absolute Distance (MAD) filter is used as a feature subset internal evaluation criterion. Class labels are not required by MAD as it discovers similarities between features according to their intrinsic properties. The upcoming sections are written as follows. In Section 2, related work is presented, while in Section 3, the proposed hybrid method is explained. In Sections 4 and 5, the experimental results and the conclusion are given respectively.