# Unsupervised Text Feature Selection Using Memetic Dichotomous Differential Evolution
## 1 Introduction
The goal of the Group-Lasso penalty is to generate sparsity in features in a grouped way. The redundancy control penalty, which measures dependency among features, is used for controlling the level of redundancy among the nominated features. Both penalty terms incorporate the L2,1-norm of weights matrix between hidden layers and input. These penalty terms are non-smooth at the source, and therefore, one efficient and simple smoothing tool was used to overcome this problem. On the other hand, unsupervised FS is not widely discussed and tested in text mining literature. In [7] it was stated that the \"curse of dimensionality\" problem as a result of high data dimensions could reduce the capability of datasets of learning algorithms and also needs high storage and computational operations. In this paper, a hybrid filter-based feature selection algorithm based on a combination of clustering and the modified Binary Ant System (BAS), named FSCBAS, was presented, to overcome the search space and high dimensional data challenges in an efficient way. The proposed model uses both local and global search techniques within and between clusters. This method is basically a combination of the genetic algorithm and simulated annealing; a damped mutation strategy was presented that prevented the search from falling into local optimum areas. Moreover, a redundancy reduction policy was adopted for the estimation of the correlation between the nominated features. It can be noticed that the method presented in [7] still uses classifiers even though it is an unsupervised feature selection method. Therefore, it is not purely considered an unsupervised method. In [8] a comprehensive and structured review of the most recently proposed unsupervised feature selection methods was presented. A taxonomy of proposed methods in that domain was given, and the main properties of these methods were highlighted. Moreover, their basic concepts were also described. In addition, a summary of the advantages and disadvantages was also given for each one of those methods. In addition, an experimental comparison was also conducted. Finally, some important challenges in this research area were discussed. The stochastic global search can be applied to perform both the supervised and unsupervised feature selection. For instance, the Genetic Algorithm (GA) is an example of the stochastic methods that have been used frequently for feature selection. However, due to the global search nature of the GA, the Memetic Algorithm (MA) was proposed. The MA has been used to hybridize the global search performed by the GA with a problem-specific local search. The local search helps to exploit the regions of interest within the space [9] MA has contributed to producing high-quality solutions in different optimization problems [10]. It is a population-based method that aims to find the best individual (agent) by exploiting and exploring the search space. There is continual research that discusses new ways to provide different algorithmic combinations using the MA to solve different scientific and engineering problems [10; 11; 12]. To the best of our knowledge, little effort has been made to optimize the unsupervised text FS for document clustering. The challenge of performing unsupervised FS is associated with the absence of referencing class labels, which makes it impossible to utilize the same validation criteria used with the supervised FS for classification. For example, in [13] a method named Meaning Based Feature Selection (MBFS) which is based on the Helmholtz principle from the Gestalt theory of human perception. Two classifiers were used to evaluate the performance of that method. However, using the classifiers in the unsupervised feature selection is not suitable due to the need to use the class tables for the classifers. Moreover, there are no standardized measures that can be used to assess the performance of unsupervised methods due to the lack of enough research in this domain as the meaning of the best feature subsets may differ across different methods.