*3.1. Experimental Setup*

In order to demonstrate the advantages of the proposed algorithm, we conducted the experiments under different numbers of training samples, and compared with several state-of-the-art ensemble learning methods, namely random forest (RF), semi-supervised feature extraction combined RF ensemble method (SSFE-RF) [22], rotation forest (RoF) [30], and rotation random forest-KPCA (RoRF-KPCA) [32]. For better comparison, the SLDA method was also used as a preprocessing step that combined with the original RoF method (we refer to it as SLDA-RoF). Finally, the LFDA and NPE methods were also used as rotation means like RoF method.

The numbers of trees were all set to *L* = 10, and the classification and regression tree (CART) was adopted as the base classifier. The numbers of features in each subset were all set to *M* = 10 for SSFE-RF, RoF, RoF-LFDA, RoF-NPE, and SSRoF. For RoRF-KPCA, Xia et al. [32] sugges<sup>t</sup> that a small number of features per subset will increase the classification performance, as such, we set *M* = 5. For RF, the number of features considered at each node was set as the square root of the used feature number. The numbers of extracted features were set equal to *M* for RoF, RoRF-KPCA, RoF-LFDA, RoF-NPE, and SSRoF. For SLDA, the number of extracted features was set to half of the original features, and other parameters were set to the same as RoF. For RoRF-KPCA, it is quite difficult to select the optimal kernel parameters. Xia et al. [32] declares that parameter tuning is needed, but different kernel functions (linear, radial basis function, and Polynomial) provide very similar results, making this choice not critical in this context. Considering the performance enhancement and the computation cost, in our experiments, we use the polynomial kernels with the degree equals to two.

The performance is evaluated by the overall accuracy (OA), and Kappa coefficient. In all cases, we conduct ten independent Monte Carlo runs with respect to the labeled training set from the ground truth images. And the results are the average values of the 10 runs. The numbers of available samples are listed in Table 1.


**Table 1.** Number of available samples in each data set.
