# Literature Review on Hybrid Evolutionary Approaches for Feature Selection
## 2 Related Work
Decision trees (DTs), support vector machines (SVMs), naive Bayes (NB), k-nearest neighbor (KNN), artificial neural networks (ANNs), and linear discriminant analysis (LDA) are just a few examples of common classifiers that have been used as wrappers in FS applications [50; 51; 52]. In the domain of filter approaches, measurements from a variety of disciplines have been incorporated, particularly information theory, correlation estimates, distance metrics, and consistency criteria [53]. Individual feature evaluation, relying on a particular aspect, is a basic filter approach in which only the best tier features are selected [50]. Relief [54] is a distinctive case in which a distance metric is applied to assess the significance of features. Filter methods are often computationally inexpensive, but they do not consider attribute relationships, which often leads to complicated problems in case of repetitive feature sets, such as in the case of microarray gene data, where the genes are intrinsically correlated [21; 53]. To overcome these issues, it is necessary to perform proper filter measurements to choose a suitable subset of relevant features in order to evaluate the whole feature set. [PERSON] et al. [55] recently published a distance measure to assess the difference between the chosen feature space and the space spanned by all features in order to locate a subset of features that approximates all features. [PERSON] et al. [56] introduced the minimum redundancy maximum relevance (MRMR) approach based on shared information, and recommended measures were added to the EC because of their powerful exploration capability [57; 58]. A unified selection approach was proposed by [PERSON] and [PERSON] [23], which optimizes multivariate performance measures but also results in an enormous search area for high-dimensional data, a problem that requires strong heuristic search methods for finding the best output. There are several relatively straightforward statistical methods, such as \(t\)-testing, logistic regression (LR), hierarchical clustering, and classification and regression tree (CART), which can be applied jointly to produce better classification results [59]. Recently, authors of [60] have applied sparse LR for FS problems including millions of features. [PERSON] et al. [24] developed a rough principle procedure to solve FS tasks under budgetary and schedule constraints. Many experiments show that most filter mechanisms are inefficient for cases with vast numbers of features [61]. 3. Number of Objectives: Single-objective (SO) optimization frameworks are techniques which combine the classifier's accuracy and the features quantity into a single optimization function. On the contrary, multiobjective (MO) optimization approaches entail techniques designed to find and balance the tradeoffs among alternatives. In an SO situation, a solution's superiority over other solutions is determined by comparing the resulting fitness values, while in an MO optimization, the dominance notion is employed to get the best results [62]. In particular, to determine the significance of the derived feature sets, in an MO situation, multiple criteria need to be optimized by considering different parameters. MO strategies thus may be used to solve some challenging problems involving multiple conflicting goals [63], and MO optimization comprises fitness functions that minimize or maximize multiple conflicting goals. For example, a typical MO problem with minimization functions can be expressed mathematically as follows,  
\[ \begin{array}{c}min\;O(x)=[o1(x),o2(x),\ldots,o_{n}(x)]\\ Sub\;to:\;g_{i}(x)\leq 0,\quad i=1,2,\ldots\ldots,m\\ h_{i}(x)=0,\quad i=1,2,\ldots,l\end{array} , \tag{1}\]  
where \(x\) is the decision variables vector, \(n\) is the number of objectives, \(o_{n}(x)\) is the \(n^{th}\) objective function and \(g_{n}(x)\) and \(h_{n}(y)\) are the problem constraints. Finding the balance among the competing objectives is the process that identifies the dominance of an MO optimization approach.