## 4 Experimental Setup
### Dataset  
Exhaustive experimentation with BEOSA was carried out using 22 benchmark and popularly available datasets [41]. These datasets have been widely used for comparative analyses of binary metaheuristic algorithms and were therefore considered suitable for testing the efficiency and performance of the method proposed in this study. Table 1 provides some information about the applied datasets. High, moderate and low-dimension datasets are included, making them suitable for experimenting with the BEOSA method on those three dimensions. This became necessary, considering the importance of investigating the suitability of an algorithm on a variety of datasets, high-dimension ones in particular, since these often have similarities with real-life binary optimization problems. The number of biomedical datasets is growing rapidly; this has led to the generation of high-dimensional features that negatively affect the classifiers of machine learning processes [42]. Many of the feature selection methods described in the literature suffer from diversity of population and local optima problems when they are evaluated against high-dimensional datasets, such as the ever-growing body of biomedical datasets. Feature selection is aimed at selecting the most effective features from an original set containing irrelevant elements; this becomes especially challenging to with high-dimensional datasets, which is why it is important for us to prove the efficacy of the BESOA with such data dimensionality. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset and References** & \begin{tabular}{c} **Number of** \\ **Features** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Instances** \\ \end{tabular} & \
\begin{tabular}{c} **Number of** \\ **Class** \\ \end{tabular} &
\begin{tabular}{c} **Description** \\ \end{tabular} \\ \hline BreastEW & 569 & 30 & 2 & Biology-based and medical-oriented dataset \\ Lung [43] & 3312 & 203 & 5 & Biology-based and medical-oriented dataset \\ CongressEW [44] & 435 & 16 & 2 & Congressional voting dataset \\ Exactly [45] & 1000 & 13 & 2 & Artificial binary classification dataset \\ Iris [46] & 4 & 150 & 2 & Biology-based dataset \\ Exactly [245] & 1000 & 13 & 2 & Artificial binary classification dataset \\ HeartEW & 270 & 13 & 2 & Biology-based and medical-oriented dataset \\ Ionosphere [47] & 351 & 34 & 2 & Electromagnetic dataset \\ Prostate & 5966 & 102 & 2 & Biology-based and medical-oriented dataset \\ Lymphography [48] & 148 & 18 & 4 & Biology-based and medical-oriented dataset \\ Mo-of-n & 1000 & 13 & 2 & Biology-based and medical-oriented dataset \\ Leukemia & 7070 & 72 & 2 & Biology-based and medical-oriented dataset \\ PenglungEW & 325 & 73 & 7 & Biology-based and medical-oriented dataset \\ Sonar & 208 & 60 & 2 & Sonar signal classification dataset \\ SpecEW [49] & 267 & 22 & 2 & Biology-based and medical-oriented dataset \\ Colon & 2000 & 62 & 2 & Biology-based and medical-oriented dataset \\ Tic-to-teo [50] & 958 & 9 & 2 & Endgrange dataset \\ Vote & 300 & 16 & 2 & Electincering domain \\ Wine [51] & 178 & 13 & 3 & Wine dataset showing the results of analysis of chemicals in wines. \\ Zoo & 101 & 16 & 7 & Biology-based dataset \\ KvSkpEW & 3196 & 36 & 2 & Game dataset \\ WaveformEW [52] & 5000 & 40 & 3 & A generator dataset generates three classes of waves, with each class sampled at 21 intervals. Additionally, each class is a random convex combining 2 out of 3 base waves. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Datasets and their corresponding details, such as the number of features, classes and instances, and a description of each. The Lung, Prostate, Leukemia, KrVsKpEW, Colon and WaveformEW datasets are considered here as high-dimensional datasets with feature sizes ranging between 4 and 7070. Additionally, most of these datasets have binary classification problems or, in the case of Lung and WaveformEW, multi-classification problems. BreastEW, Exactly, Exactly2, M-of-n and Tic-tac-toe are medium-sized dimensional datasets.