## 5 Experimental Results
### Datasets Used  
MBHO was validated using fourteen publicly available datasets. These datasets were sourced from the UCI ML repository [[http://archive.ics.uci.edu/](http://archive.ics.uci.edu/)]([http://archive.ics.uci.edu/](http://archive.ics.uci.edu/)) (accessed on 30 May 2024) UCI ML Repository. The datasets are colon, Darwin, Divorce, WDBC, Leukemia, Leukemia-3c, MLL, Parkinson's, Sobar, Sonar, SPECTFtest, SRBCT, Urban, and WPBC. The colon dataset is widely used in binary classification, especially in predicting colon cancer. The Darwin dataset is widely used in ML tasks such as classification and regression analysis. The Leukemia and Leukemia-3c datasets demonstrate various molecular or cellular features that are widely used in machine learning tasks and differ in size. The MLL dataset includes various molecular or cellular features widely utilized in data science tasks. The WDBC dataset represents various diagnostic measurements crucial for breast cancer research and analysis. The SRBCT dataset embodies diverse molecular or cellular attributes pertinent to machine learning research and analysis in the context of small, round, blue-cell tumors. The Sobar dataset encapsulates diverse socio-behavioral features, facilitating research and analysis in relevant domains. The Parkinson's dataset represents various clinical and demographic features relevant to [PERSON]'s disease research and diagnosis. The Sonar dataset represents distinct acoustic signal features valuable for research and analysis in underwater target detection or classification tasks. The Divorce dataset represents diverse socio-demographic and relationship features crucial for machine learning research and analysis in understanding factors valuable in divorce prediction or prevention. The SpectTF dataset embodies various attributes related to spectral analysis or signal-processing pertinent to research and analysis in fields such as pattern recognition or medical diagnostics. The Urban dataset represents diverse urban features, facilitating research and analysis in urban planning, development, or sustainability studies. The WPBC dataset represents various clinical and demographic features pertinent to research and analysis in the context of breast cancer prognosis and treatment. As detailed above, this diverse selection of datasets with different applications helped us better generalize the performance of our FS approach for different datasets with different characteristics. A detailed description of all these datasets can be found in Table 1. These datasets were specifically selected due to certain characteristics such as high feature sizes and binary classes, which are ideal for demonstrating the effectiveness of feature selection algorithms (both our proposed one and those used as baselines in our evaluation). We underscore that the benefits of our suggested model apply to any given dataset with many features that need a careful and accurate FS approach. It is worth mentioning that we employed various metrics (refer to Section 5.4) for evaluation purposes, such as the area under the curve (AUC), which is applicable only for binary and balanced datasets. For instance, the _AUC_ is Nan for the Leukemia and Sobar datasets and the Leukemia-3c dataset, as the former are imbalanced, and the latter is not binary.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Name** & **No. of Attributes** & **No. of Instances** & **No. of Classes** \\ \hline Colon & 2000 & 62 & 2 \\ Darwin & 450 & 174 & 2 \\ Leukemia & 3571 & 72 & 2 \\ Leukemia-3c & 7129 & 72 & 3 \\ MLL & 12,582 & 72 & 3 \\ WDBC & 30 & 569 & 2 \\ SRBCT & 2308 & 83 & 4 \\ Sobar & 19 & 72 & 2 \\ Parkinson’s & 22 & 197 & 2 \\ Sonar & 60 & 208 & 2 \\ Divorce & 54 & 170 & 2 \\ SpectTF & 44 & 267 & 2 \\ Urban & 146 & 675 & 9 \\ WPBC & 31 & 198 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main descriptions of the 14 datasets used, including the number of features, number of instances, and number of classes.