## 3 Harmony Search-Based Parameter Optimization (HASPO) Approach
### Harmony Search-Based Parameter Optimization of JIT-SDP
\begin{table}
\begin{tabular}{c c c c c} \hline
**Classifier** & **Parameters** & **Candidate Search Space** & **Range** & **Interval** \\ \hline \multirow{5}{*}{Decision Tree} & criterion (C\({}_{\text{r}}\)) & [\({}^{\text{\text{\tiny{r}}}}\)gini\({}^{\text{\tiny{r}}}\), “entropy”], default = \({}^{\text{\tiny{r}}}\)gini\({}^{\text{\tiny{r}}}\) & C\({}_{\text{r}}\) = [\({}^{\text{\tiny{r}}}\)gini\({}^{\text{\tiny{r}}}\), “entropy”] & - \\ \cline{2-5}  & splitter (S\({}_{\text{p}}\)) & [\({}^{\text{\text{\tiny{r}}}}\)best”, “random”], default = \({}^{\text{\tiny{r}}}\)best” & S\({}_{\text{p}}\) = [\({}^{\text{\tiny{r}}}\)best”, “random”] & - \\ \cline{2-5}  & max\_depth (D\({}_{\text{max}}\)) & int, default = None & \({}^{\text{\tiny{5}}}\)\(\leq\) D\({}_{\text{max}}\)\(\leq\) 50, 0: Unlimited & 5 \\ \cline{2-5}  & min\_samples\_split (SS\({}_{\text{min}}\)) & int or float, default = 2 & \({}^{\text{\tiny{2}}}\)\(\leq\) SS\({}_{\text{min}}\)\(\leq\) 10 & 1 \\ \cline{2-5}  & min\_samples\_leaf (SL\({}_{\text{min}}\)) & int or float, default = 1 & \({}^{\text{\tiny{1}}}\)\(\leq\) SL\({}_{\text{min}}\)\(\leq\) 10 & 1 \\ \cline{2-5}  & min\_weight\_fraction\_leaf (WL\({}_{\text{min}}\)) & float, default = 0.0 & 0.0 \(\leq\) WL\({}_{\text{min}}\)\(\leq\) 0.5 & By HSA \\ \cline{2-5}  & max\_features (F\({}_{\text{max}}\)) & int, float or [\({}^{\text{\tiny{r}}}\)auto”, “sqrt”], “log2”], default = None & \({}^{\text{\tiny{1}}}\)\(\leq\) F\({}_{\text{max}}\)\(\leq\) Max, \({}^{\text{\tiny{r}}}\)\(\leq\) Number of features & 1 \\ \cline{2-5}  & max\_leaf\_nodes (L\({}_{\text{max}}\)) & int, default = None & \({}^{\text{\tiny{1}}}\)\(\leq\) L\({}_{\text{max}}\)\(\leq\) 100, 0: Unlimited & 10 \\ \cline{2-5}  & min\_impurity\_decrease (I\({}_{\text{min}}\)) & float, default = 0.0 & 0.0 \(\leq\) I\({}_{\text{min}}\)\(\leq\) 1.0 & By HSA \\ \cline{2-5}  & class\_weight (W\({}_{\text{c}}\)) & \begin{tabular}{c} dict, list of dict or “balanced”, \\ default = None \\ \end{tabular} & 0.0 \(\leq\) W\({}_{\text{c}}\)\(\leq\) 1.0 for each \\ \cline{2-5}  & \begin{tabular}{c} c c c} \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ ccp\_alpha (C\({}_{\text{a}}\)) & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} & \multicolumn{2}{c}{} \\ \hline \multirow{5}{*}{Bagging} & \begin{tabular}{c} n\_estimators (E\({}_{\text{num}}\)) \\ \end{tabular} & \
\begin{tabular}{c} int, default = 10 \\ \end{tabular} & \({}^{\text{\tiny{1}}}\)\(\leq\) E\({}_{\text{num}}\)\(\leq\) 100 & 5 \\ \cline{2-5}  & max\_samples (S\({}_{\text{max}}\)) & int or float, default = 1.0 & 0.0 \(\leq\) S\({}_{\text{max}}\)\(\leq\) 1.0 & By HSA \\ \cline{2-5}  & max\_features (F\({}_{\text{max}}\)) & int or float, default = 1.0 & 1 \(\leq\) F\({}_{\text{max}}\)\(\leq\) Max & 1 \\ \cline{2-5}  & bootstrap (B) & bool, default = True & B = [True, False] & - \\ \cline{2-5}  & bootstrap\_features (B\({}_{\text{f}}\)) & bool, default = False & B\({}_{\text{f}}\) = [True, False] & - \\ \cline{2-5}  &
\begin{tabular}{c} oob\_score (O) \\ \end{tabular} & bool, default = False & O = [True, False] & - \\ \hline \end{tabular}
\end{table} 
Table 2: Search space, range, and interval of harmony search. The detailed process of HS-based parameter optimization is as follows:  
1.