# Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search
## 1 Introduction
Recently, to enhance its performance, modified [24] and hybrid [25] versions of the SOS algorithm have been developed as an alternative to the initial SOS algorithm proposed by [22]. Ref. [20] addressed the supervised feature-selection issue for 19 datasets from the UCI repository using the binary version of the SOS algorithm. The results indicated that, for the majority of datasets, the binary SOS algorithm may achieve a high classification accuracy with the fewest characteristics. The SOS algorithm has also been used to solve multi-objective problems in optimization. A multi-objective symbiotic organism search technique based on the weighted-sum method was proposed by [26] as a supervised learning method for economic/emission dispatch problems in power systems. The proposed method has been found to outperform other optimization algorithms such as the genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), the bees algorithm (BA), the mine blast algorithm (MBA), ant colony optimization (ACO), and cuckoo search (CS). The application of SOS algorithms has since increased, particularly in the engineering field [27]. Though unsupervised learning has the capability to improve computational efficiency and retrieval recall, very few studies has been carried out in the literature specifically addressing unsupervised learning problems such as feature selection and clustering [27; 28]. Previous studies concentrated on identifying optimal feature selection for brain-computer interfaces [26] and satellite image classification issues [29]. Within the literature, [PERSON]. [30; 31] explored text clustering and feature selection utilizing the SOS method. In their empirical research, [PERSON]. [32] addressed text classification problems, and [31] proposed an SOS-based approach for feature extraction issues. Though the literature provides a larger proportion of works on single-objective approaches than on multi-objective optimization methods, it is observed that multi-objective optimization methods for FS problems based on metaheuristics techniques are not sufficiently examined [11]. Non-dominated sorting GA II (NSGA-II) or its variants form the basis of the majority of multi-objective techniques [33; 34; 35; 36]. Other evolutionary computation (EC) approaches used in multi-objective feature selection include DE [37], ACO [38], and PSO [39]. According to the results of all these studies, multi-objective optimization algorithms outperform single-objective techniques in terms of both the quantity of features needed for supervised learning and classification performance. However, the existing literature has predominantly focused on datasets of modest to intermediate scale, indicating that the multi-objective feature-selection problem remains an unexplored field of study for unsupervised learning, much like high-dimensional data clustering. In addition, given that multi-objective evolutionary computation algorithms used for the FS problem are based on conventional algorithms like ACO, PSO, and GA, which typically have significant drawbacks such as slow convergence rate, high computational complexity, and trapping into local optima, there is also a need to investigate a novel multi-objective algorithm's capability to handle the feature-selection problems [33; 40]. Although feature selection has been studied extensively, the literature review indicates that multi-objective unsupervised learning for two problems--unsupervised feature selection and clustering--has received relatively less attention. Furthermore, existing multi-objective research faces many of the aforementioned issues and has not addressed large-scale datasets such as TDT TREC data. This study proposes a multi-objective algorithm with a wrapper-based approach for data clustering, taking into account the shortcomings of the existing literature and the benefits of the SOS method.