# Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data
## 5 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c} \hline
**Dataset** & **ANOVA** & **EN** & **BFSO** & **IG** & **CSA** & **PSO4-2** & **CS** & **RR** & **GA** & **ICPSO** & **GFSO** \\ \hline LUSC & 8.344077873 & 8.046691179 & 215.7968326 & 116.1854684 & 5.728859017 & 756.1332397 & **0.201476097** & 0.22044441 & 2061.760082 & 11.760.040000 & 4727.80717 \\ \hline PRAD & 8.281743498 & 8.080709219 & 71.73043895 & 119.4153876 & 6.43004775 & 579.0874655 & **0.162760019** & 0.233434916 & 1704.856312 & 8977.9900000 & 3468.037741 \\ \hline THCA & 7.023365498 & 6.38810997 & 103.21277558 & 96.1323471 & 5.47678926 & 1001.062687 & **0.132241011** & 0.161581335 & 1496.190809 & 6857.7800000 & 3996.668985 \\ \hline KIKPI & 1.0215051801 & 1.89077414 & 4.85510195 & 62.40318899 & 3.0199882 & 28.756426 & 0.07776888 & 0.080544043 & 513.1460321 & 1580.800000 & 578.6727921 \\ \hline STAD & 6.801307201 & 6.5065525 & 116.1693333 & 10.4697474 & 6.506106976 & 9.10941431 & **0.1222729** & 10.5305401 & 275.5410000 & 4584.99828 \\ \hline KIRP & 5.174865467 & 4.74374376 & 96.44906092 & 91.94907924 & 43.3138036 & 5558.1365920 & 0.100507519 & **0.09289854** & 1163.142314 & 3697.300000 & 3026.042607 \\ \hline KIRC & 9.10013899 & 8.821824551 & 219.8091596 & 125.1197909 & 4.91203667 & 439.0428246 & **0.197215673** & 0.277624906 & 1111.391231 & 6499.1700000 & 1929.743119 \\ \hline LIRC & 5.15997694 & 5.28027752 & 93.49406451 & 81.8985223 & 4.9018607 & 386.6665111 & 0.131821777 & **0.101901607** & 1560.00000 & 1481.1170000 & 2827.767136 \\ \hline PRSC & 6.716243035 & 6.69004624 & 17.0767737 & 93.95051628 & 4.944217473 & 50.50531957 & **0.17227255** & 0.11735373 & 68.84110465 & 1282.493000 & 2802.933625 \\ \hline CHOL & 1.19290996 & 1.373466492 & 29.340401317 & 57.9781331 & 5.79638557 & 6.654745416 & 0.08827616 & **0.033374468** & 184.4370544 & 1768.1600000 & 393.1424497 \\ \hline UCEC & 6.922133207 & 6.82439749 & 27.7994627 & 67.69461523 & 5.22724159 & 558.3500606 & **0.13346052** & 0.191025019 & 1747.461532 & 5680.600000 & 5587.02812 \\ \hline COAD & 6.522157074 & 6.2335757828 & 106.4919822 & 93.469239 & 4.649344683 & 223.7496894 & **0.13974833** & 0.172054291 & 1252.5308542 & 8167.4100000 & 1559.296618 \\ \hline \end{tabular}
\end{table} 
Table 7: Execution time in seconds. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline
**Dataset** & **All** & **ANOVA** & **EN** & **BFSO** & **IG** & **CSA** & **PSO4-2** & **CS** & **RR** & **GA** & **ICPSO** & **GFSO** \\ \hline LUSC & 43,696 & 4489 & 4489 & 11,388 & 13,968 & 4336 & 4369 & 10,924 & 4589 & 21,833 & 37 & **167** \\ \hline PRAD & 37,676 & 4482 & 4482 & 3685 & 12,137 & 3033 & 3767 & 9419 & 7753 & 18,813 & 16 & 102 \\ \hline TIRCA & 35,957 & 3611 & 3611 Tables 9-12 show the results of the Mann-Whitney U Test comparing GPSO with all other algorithms in terms of accuracy, precision, recall, and F1-score. The analysis revealed several findings regarding the performance of GPSO compared to other algorithms. Based on these results, GPSO shows statistically significant differences in accuracy compared to 9 out of the 10 other algorithms, with only BPSO showing a non-significant difference (though still trending toward significance with \(p=0.0883\)). The very low \(p\)-values  
Figure 4: Recall box plot. Figure 3: Precision box plot. against several algorithms (particularly PSO(4-2) and IGPSO) suggest that GPSO performs either significantly better or significantly worse than these algorithms. For precision, GPSO shows statistically significant differences compared to 5 out of the 10 other algorithms (PSO(4-2), CS, RR, GA, and IGPSO). The differences with CSA are close to significance (\(p=0.0847\)). The remaining algorithms (ANOVA, EN, BPSO, and IG) do not show statistically significant differences in precision when compared with GPSO.