## 4 Results
### Appropriateness Evaluation for Base Learners  
The appropriateness of a base learner to the ensemble was evaluated mainly through the correlation analysis. However, in order to verify the improved role of the stacking-based ensemble, the performance of its single counterparts integrated into the whole ensemble framework still needed to be presented. In the stage of resampling, a series of predictive accuracies were recorded for each base learner as the subset was constantly sampled. To observe the accuracy distribution, a density curve was created by plotting the accuracy value on the x-axis and the corresponding frequency of the accuracy on the y-axis. Figure 7 shows the density curves for all base learners in terms of 20, 30, and 40 resamples, respectively. In general, it is preferable to use a specific value to describe a model's performance. Thus, the mean accuracy (MACC) was used to quantify the performance of base learners against the ensemble. Regarding the 20 resamples, the ANN showed the highest MACC with a value of 84.20%, followed by the SVM (83.92%), NB (82.63%), and LR (81.00%), respectively. In the case of 30 resamples, the highest MACC was also achieved by the ANN (84.49%), followed by the SVM (84.01%), NB (82.36%), and LR (81.26%), respectively. As for 40 resamples, the results showed a similar ranking, as the ANN gained the highest MACC value of 84.38%, followed by the SVM (83.92%), NB (82.68%), and LR (81.17%), respectively. Consistently, the results demonstrated the ANN had a slightly better performance than the SVM, NB, and LR. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Conditioning** \\ **Factors** \\ \end{tabular} } & \multirow{2}{*}{**Classes**} & **Numbers of** & \multirow{2}{*}{**Numbers of** **Non- Landslide**} & **IV of** & **IV of** \\  & & **Pixel** & **Pixel** & **Classes** & **Factors** \\ \hline \multirow{8}{*}{NDVI} & \(-\)1–0.12 & 10 & 28,751 & 0.087 & \multirow{8}{*}{\(1.089\)} \\  & 0.12–0.35 & 97 & 19,308 & 0.282 & \multirow{8}{*}{\(1.089\)} \\  & 0.35–0.58 & 144 & 50,805 & 0.168 & \\  & 0.58–0.75 & 62 & 38,833 & 0.006 & \\  & 0.75–0.89 & 38 & 39,805 & 0.008 & \\  & 0.89–1 & 37 & 132,714 & 0.539 & \\  & 0–583.52 & 226 & 101,844 & 0.148 & \\ Distance to & 583.52–1782.99 & 125 & 83,965 & 0.009 & \\ faults(m) & 1782.99–2317.76 & 18 & 18,050 & 0.004 & \\  & \(>\)2317.76 & 19 & 106,357 & 0.572 & \\  & 1 & 131 & 110,304 & 0.001 & \\  & 2 & 0 & 335 & 0.007 & \\  & 3 & 104 & 63,313 & 0.017 & \\ ERG & 4 & 36 & 26,766 & 0.001 & 0.396 \\  & 5 & 52 & 21,698 & 0.042 & \\  & 6 & 30 & 81,418 & 0.226 & \\  & 7 & 35 & 6382 & 0.103 & \\  & 0–6.22 & 18 & 5105 & 0.035 & \\ Slope angle (\({}^{\circ}\)) & 6.22–21.27 & 126 & 63,154 & 0.054 & \\  & 21.27–32.86 & 156 & 121,863 & 0.000 & 0.176 \\  & \(>\)32.86 & 88 & 120,094 & 0.086 & \\ Profile curvature & \(-\)16.00–0.04 & 106 & 130,893 & 0.068 & \\  & \(-\)0.04–26.53 & 282 & 179,323 & 0.037 & 0.105 \\ Slope aspect (\({}^{\circ}\)) & 0–302.33 & 358 & 262,403 & 0.007 & \\  & \(>\)302.33 & 30 & 47,813 & 0.057 & 0.065 \\ Annual rainfall & 773.87–1210.57 & 292 & 202,052 & 0.015 & \\ (mm) & 1210.57–1419.27 & 96 & 108,164 & 0.037 & \\ Distance to rivers & 0–272.17 & 199 & 124,775 & 0.027 & \\ (m) & 272.17–2566.56 & 189 & 185,441 & 0.023 & 0.050 \\ Plan curvature & \(-\)28.56–20.05 & 388 & 310,216 & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._The scatter-plot matrix of the accuracy belonging to corresponding pairs of base learners is shown in Figure 8. This allowed for a pair-wise comparison to base learners. The correlation between base learners is an estimation of how closely the scatter points of the accuracy lie to the straight line. Visual inspection of Figure 8 shows that the scatter belonging to the pair of SVM and LR was roughly linear. The PCC provided a quantitative measure for this correlation, and its result revealed that the highest correlated pair was the SVM and LR regardless of 20, 30, or 40 resamples, with PCC values of 0.75, 0.73, and 0.68, respectively.