# Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data
## 6 Conclusions  
In this paper, we proposed a feature selection method based on Particle Swarm Optimization. The proposed algorithm makes use of a guided particle scheme whereby three filter-based methods are incorporated. The proposed algorithm addressed the issue of premature convergence to global optima compared to other PSO feature-based methods by the expansion of the search space using three filter-based methods to generate particles, and by using a fitness factor that weights accuracy and size of feature set including knowledge transfer. The proposed method was compared to state-of-the-art feature selection algorithms. ANOVA, EN, BPSO, IG, CSA, PSO(4-2), CS, RR, GA, and IGPSO were implemented and experimented with. The 12 genome datasets we used included up to 44,909 features, which is consider high-dimensional data. The results as well as statistical analysis show that the proposed algorithm compares better to other state-of-the-art feature selection  
\begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **U Statistic** & \(p\)**-Value** \\ \hline ANOVA & 51.5 & 0.2394 \\ EN & 42.0 & 0.0826 \\ BPSO & 66.5 & 0.7687 \\ IG & 47.5 & 0.1581 \\ CSA & 39.0 & 0.0575 \\ PSO(4-2) & 52.0 & 0.2582 \\ CS & 49.5 & 0.2006 \\ RR & 48.5 & 0.1809 \\ GA & 35.0 & 0.0329 \\ IGPSO & 42.0 & 0.0871 \\ \hline \end{tabular}
\end{table}
Table 12: Mann–Whitney U test results—F1-score.  
\begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **U Statistic** & \(p\)**-Value** \\ \hline ANOVA & 55.5 & 0.3465 \\ EN & 42.0 & 0.0826 \\ BPSO & 76.5 & 0.8140 \\ IG & 50.5 & 0.2168 \\ CSA & 39.0 & 0.0575 \\ PSO(4-2) & 58.0 & 0.4337 \\ CS & 61.0 & 0.5411 \\ RR & 59.5 & 0.4850 \\ GA & 39.0 & 0.0575 \\ IGPSO & 51.5 & 0.2460 \\ \hline \end{tabular}
\end{table}
Table 11: Mann–Whitney U test results—recall.  
algorithms. In summary, GPSO achieves competitive or better accuracy and precision compared to most algorithms, with similar recall and some variation in F1-score. This indicates its strength in balancing accuracy and precision while maintaining performance on other metrics.  
Future work will involve conducting additional experiments to further evaluate the robustness and adaptability of the knowledge-transfer mechanism across diverse datasets and problem domains. This should include fine-tuning the parameters and design of the mechanism to enhance its effectiveness and efficiency. Additionally, a systematic comparison with alternative knowledge-transfer solutions, such as transfer learning frameworks, meta-learning approaches, and domain adaptation techniques, could be performed. Moreover, exploring hybrid approaches that combine multiple knowledge-transfer techniques may lead to improved performance.  
This research is based upon work supported by the National Science Foundation under Grant EEC-2050175. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.  
The datasets used in this study were extracted from [24].  
This work used resources of the Center for Computationally Assisted Science and Technology (CCAST) at North Dakota State University, which were made possible in part by NSF MRI Award No. 2019077.  
The author declares no conflicts of interest.