## 3 Experiment Results
### Datasets  
In this study, we conducted an evaluation of our proposed algorithm using three microarray datasets with high dimensionality from the breed Shandong black pig, a popular pig breed in Laiwu, Shandong, China, known for its tender and fine meat. These datasets were obtained from the Life Science Research Institute of Qingdao Agricultural University and were used to analyze intricate biological data and identify relevant genetic information. Remarkably, all datasets contained tens of thousands of features, surpassing the number of samples by a substantial margin, as shown in Table 1.  
The BPEx dataset consists of continuous gene expression data for Shandong black pigs. It comprises 181 samples, with each having 24,368 expression features. The target class of the dataset is pork quality, categorized into four levels: excellent (22 samples), good (49 samples), average (76 samples), and inferior (34 samples). Analyzing this dataset can help identify genes associated with high-quality pork and provide valuable insights for improving pork quality through breeding and production strategies.  
The BPSnp dataset comprises discrete gene single nucleotide polymorphisms (SNPs) for Shandong black pigs. It includes 236 samples, each having 27,268 SNP features. This dataset has three target classes:  
1. Body size and weight are categorized into five levels: very large (49 samples), large (61 samples), medium (59 samples), short (46 samples), and very short (21 samples).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Acronym** & **Raw Data Type** & **Feature Number** & **Sample Number** & **Class Number** \\ \hline BlackPic Expression & BPEx & Continuous & 24,368 & 181 & 4 \\ BlackPic SNP & BPSnp & Discrete & 27,268 & 236 & 14 \\ BlackPic PRRS & BPRRS & Continuous & 29,768 & 151 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Microarray datasets of Shandong black pig.  
2. The ratio of muscle to fat is categorized into five levels: AAA (46 samples), AA (53 samples), A (61 samples), B (47 samples), and C (29 samples).
3. The growth rate is categorized into four levels: rapid growth (57 samples), fast growth (82 samples), moderate growth (62 samples), and slow growth (35 samples).  
Analyzing the BPSnP dataset can provide insights into the genetic factors influencing body size, weight, muscle-to-fat ratio, and growth rate in Shandong black pigs, potentially contributing to improved breeding and production strategies for these traits.  
The BPPRRS dataset consists of gene expression data specifically focused on porcine reproductive and respiratory syndrome (PRRS), a highly prevalent viral disease in pigs worldwide that causes significant losses in the swine industry. This dataset includes 29,768 expression features. The dataset is divided into two categories: infected (90 samples) and normal (61 samples). With its detailed gene expression profiles, the BPPRRS dataset serves as a critical resource for researchers aiming to understand the molecular mechanisms underlying PRRS, identify potential genetic markers for resistance or susceptibility to the disease, and develop effective strategies for breeding and managing pigs to minimize the impact of PRRS on pork production.  
Due to the excessively large number of features in the datasets, it is imperative that FS algorithms are highly efficient to handle the computational complexity and resource requirements effectively.