# Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search
## 2 Review of Related Works
### Global-Search Unsupervised Feature-Selection Algorithms Based on SOS Methods  
In the context of data mining and machine learning, unsupervised feature-selection methods have attracted a number of research interests as a result of their capability to identify and select relevant features without relying on class-label information. Feature selection aims to identify the relevant characteristics and remove the irrelevant features from the dataset in order to achieve benefits such as a decrease in the dimensionality of the data, an improvement in the performance of classification methods, and helps in the learning process [26]. However, it is noteworthy that there are two types of feature extraction approaches: wrapper-/coating-based and filter-based methods [21]. Coating-based methods use classification algorithms as a criterion for selecting the best possible solutions. Conversely, filter-based methods show a reasonable level of computing efficiency and are not dependent on any particular algorithm. Researchers argue that since coating-based systems incorporate classification algorithms in the evaluation criteria, they perform better than filter-based methods [44; 45]. In light of this background, the authors [21] offer different wrapper-based binary techniques that use the SOS method to solve feature-selection issues. BSOSST was first utilized, while BSOSVT was used as the second model. The binary SOS (BSOS) is developed using these two coating-based approaches. In the third method, EEBCSOS demonstrated its efficacy and exploratory capabilities. In order to address the problem of feature selection, [PERSON]. [46] presented a novel technique called the improved binary symbiotic organism search (IB-SOS), which makes use of the wrapper method. To preserve the delicate balance between exploration and exploitation, the authors also included the same biological symbiosis approaches that are used in the continuous SOS method in the proposed IBSOS technique. The NSMOSOS algorithm, a wrapper-based multi-objective algorithm, was introduced by [26] to generate the ideal feature subset. The study evaluated a brain-computer interface (BCI) system's effectiveness and robustness for motor-imaging feature selection across two datasets, achieving the highest accuracy results for both. In a recent study, [PERSON]. [31] introduced an innovative feature-selection technique to extract the most relevant features from extensive input data, using the BSOS metaheuristic to enhance email spam detection. Research by [20] demonstrated that the BSOS algorithm could identify the minimal set of features across various datasets while maintaining high classification accuracy. However, the Bayesian structural optimization method (BSOS) has limitations with low-dimensional datasets and reduced sensitivity in high-dimensional datasets. Additionally, [PERSON]. [47] proposed a new feature-selection approach employing the SOS method to enhance the accuracy and effectiveness of sleep staging by using physiological data to classify sleep stages. To cope with high-dimensional feature-selection problems, parallel multi-objective optimization approaches were also proposed by [48]. The proposed multi-objective evolutionary alternative (MOEA) approaches were implemented on EEG signals for brain-computer interface (BCI) benchmarks, which show the superior results in terms of hypervolume and speedup. When comparing performance, wrapper methods generally surpass filter methods. However, for high-dimensional datasets like microarray datasets, where sample sizes are smaller than the feature dimensions, the wrapper method can become computationally intensive. To address this, researchers have developed hybrid strategies that integrate both wrapper and filter techniques [48; 49]. The authors tested five distinct discrete SOS algorithms [48]. These techniques aim to improve classification accuracy by optimizing the neighborhood sizes of the k-nearest neighbor method and feature subsets through a two-step process.