## 5 IRSA for Solving Classification Problems
#### Statistical Indicators for Classification  
Precision, recall, and \(F1\) score are the parameters used to evaluate the performance of the different techniques used in this paper. Precision and recall are defined in (29) and (30) respectively:  
\[Precision=\frac{TP}{TP+FP} \tag{29}\]  
\[Recall=\frac{TP}{TP+FN} \tag{30}\]  
where \(TP\) (true positive), \(FP\) (False Positive), and \(FN\) (False Negative) are determined by computing the confusion matrix. The ideal value for precision and recall is one. The \(F1\) score can be defined as:  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{31}\]  
Table 10 represents the statistical results for the eight selected datasets. The precision, Recall, and \(F1\) of the proposed method were higher for six out of the eight datasets. Considering all the parameters mentioned above, we can conclude that the proposed IRSANN method provides optimum results and outperforms all other comparable MH techniques. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Dataset**} & \multicolumn{6}{c}{**Cost Function Comparison**} \\ \cline{2-7}  & & **PSONN** & **BMOANN** & **AOANN** & **RSANN** & **IRSANN** \\ \hline \multirow{4}{*}{Iris} & Best & 0.078788 & 0.082034 & 0.342085 & 0.143403 & 0.072572 \\ \cline{2-7}  & Avg & 0.137949 & 0.087161 & 0.742686 & 0.327073 & **0.115797** \\ \cline{2-7}  & Std & 0.083667 & 0.00569 & 0.484778 & 0.159949 & 0.108159 \\ \hline \multirow{4}{*}{Heart} & Best & 0.56952 & 0.545925 & 0.995477 & 0.808033 & 0.472722 \\ \cline{2-7}  & Avg & 0.640322 & 0.581627 & 1.372587 & 0.853887 & **0.517036** \\ \cline{2-7}  & Std & 0.062089 & 0.030938 & 0.53151 & 0.076074 & 0.038613 \\ \hline \multirow{4}{*}{Stress-Lysis} & Best & 0.156856 & 0.136333 & 0.321634 & 0.248556 & 0.0911631 \\ \cline{2-7}  & Avg & 0.159272 & **0.142592** & 0.353229 & 0.627416 & 0.268016 \\ \cline{2-7}  & Std & 0.003418 & 0.007051 & 0.027562 & 0.328421 & 0.055127 \\ \hline \multirow{4}{*}{Banknote-authentication} & Best & 0.318751 & 0.154256 & 0.556654 & 0.503623 & 0.085457 \\ \cline{2-7}  & Avg & 0.331262 & 0.170783 & 0.764328 & 0.836328 & **0.145555** \\ \cline{2-7}  & Std & 0.017693 & 0.016899 & 0.207149 & 0.288424 & 0.054529 \\ \hline \multirow{4}{*}{Blood-transfusion} & Best & 0.823567 & 0.882403 & 0.960614 & 0.958203 & 0.887161 \\ \cline{2-7}  & Avg & **0.830233** & 0.888582 & 1.260426 & 0.976322 & 0.895859 \\ \cline{2-7}  & Std & 0.009426 & 0.008437 & 0.259695 & 0.015692 & 0.009459 \\ \hline \multirow{4}{*}{Cryotherapy} & Best & 0.357101 & 0.756437 & 0.234832 & 0.542086 & 0.28827 \\ \cline{2-7}  & Avg & 0.362329 & 0.888982 & 0.292911 & 0.764291 & **0.305118** \\ \cline{2-7}  & Std & 0.007394 & 0.129127 & 0.050999 & 0.192532 & 0.127465 \\ \hline \multirow{4}{*}{Diabetes} & Best & 1.048615 & 0.725924 & 0.730482 & 0.947266 & 0.646058 \\ \cline{2-7}  & Avg & 1.076385 & 0.743759 & 0.7562 & 0.982109 & **0.724891** \\ \cline{2-7}  & Std & 0.042824 & 0.019733 & 0.036372 & 0.040968 & 0.07128 \\ \hline \multirow{4}{*}{Haberman} & Best & 0.892895 & 0.906456 & 0.977251 & 0.994621 & 0.881229 \\ \cline{2-7}  & Avg & 0.936445 & 0.931774 & 1.269601 & 0.998591 & **0.925252** \\ \cline{2-7}  & Std & 0.061589 & 0.00719 & 0.14037 & 0.003469 & 0.038883 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Cost evaluation of classification dataset.