# Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data
## 5 Results  
This section presents the results of the experiments conducted. Table 3 shows the accuracy results obtained running all algorithms on all datasets. The best values achieved for each dataset is highlighted in bold. As can be seen, our proposed algorithm as well as the BPSO obtains the highest accuracy for four datasets, the ANOVA, EN, and IG algorithms achieve the highest accuracy for three dataset, the CSA and GA algorithms for two datasets, and the CS and RR algorithms score best for one dataset. \begin{table}
\begin{tabular}{l l} \hline \hline
**Algorithm** & **Parameters** \\ \hline GA & \(num\_iterations=100\), \(population\_size=30\), \\  & \(mutation\_rate=0.01\), \(crossover\_rate=0.9\) \\ \hline BPSO & \(max\_iterations=\min(50,num\_features\times 5)\), \\  & \(num\_particles=min(30,num\_features\times 2)\), \\  & \(w=0.5\), \(c_{1}=2.0\), \(c_{2}=2.0\) \\ \hline PSO(4-2) & \(max\_iterations=100\), \(num\_particles=30\), \\  & \(w=0.7298\), \(c_{1}=1.49618\), \(c_{2}=1.49618\) \\ \hline IGPSO & \(max\_iterations=100\), \(num\_particles=30\), \(w=0.9\), \\  & \(c_{1}=2.0\), \(c_{2}=2.0\), \(hidden\_layers=100\), \(learning\_Rate=0.001\) \\ \hline GPSO & \(max\_iterations=100\), \(num\_particles=30\), \\  & \(w=0.5\), \(c_{1}=1.5\), \(c_{2}=1.5\), \(\gamma=0.95\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters used for the algorithms. As for the precision results given in Table 4, the proposed algorithm scores highest for seven datasets, BPSO scores best for five datasets, ANOVA and EN for three datasets, IG, CSA, and IGPSO for two datasets, and CS and RR for one dataset. Next up are the recall results given in Table 5. As can be seen from the results in the table, GPSO again scores best for six datasets, followed by BPSO for five datasets, ANOVA for four datasets, EN and IG for three datasets, CSA and GA for two datasets, and PSO(4-2), CS, and RR for one dataset. F1-scores are displayed in Table 6. The table shows that the proposed algorithm, GPSO, obtains the best results for seven datasets followed by BPSO for five datasets, ANOVA, EN, and IG for three datasets, CSA and GA for two datasets, and PSO(4-2), CS, RR, and IGPSO for one dataset. Figures 2-5 show the accuracy, precision, recall, and F1-score results as box plots. Please note that numbers in bold indicate the best results. In order to evaluate the different algorithms fairly, we are investigating their execution times as well. Table 7 lists the running time/execution time of all algorithms run on all datasets. As can be seen, the fastest algorithms are CS followed by RR taking our proposed algorithm. However, as we have seen, both algorithms do not score very highly in terms of accuracy, precision, recall, and F1-score. The IGPSO takes the longest, with an average of 10,790.84 s (179.85 min), followed by our algorithm, with an average of 2919.84 s (48.66 min). However, our algorithm scores overall better based on the other measures. Not unexpected, the third slowest algorithm is GA followed by PSO(4-2) and BPSO. In terms of the number of features used and selected during the feature selection process, Table 8 shows the results. As can be seen, IGPSO uses by far the fewest numbers of features during the process followed by GPSO. All other algorithms select and use more features for the classification task. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline
**Dataset** & **ANOVA** & **EN** & **BPSO** & **IG** & **CSA** & **PSO4-2** & **CS** & **RR** & **GA** & **IGPSO** & **GPSO** \\ \hline LUSC & 0.678260870 & 0.647619048 & 0.963853022 & 0.543703704 & 0.48184184181 & 0.79