## 8 Conclusions  
The main objective of this work was to evaluate the performance of the novel self-optimizing probabilistic Vectorized Earth Observation Retrieval (VEOR) classifier with respect to common machine learning algorithms with fine-tuned hyper-parameters and optimized input features. This was achieved within a series of experiments with synthetic datasets, as well as with daytime cloud discrimination using AQUA MODIS spectral measurements and a reference cloud mask derived from combined CALIOP lidar and CPR radar data. The classification results proved that the VEOR method is able to accurately classify large datasets commonly occurring within various Earth Observation (EO) disciplines. However, it is not suitable for small and noisy datasets due to the imprecise reconstruction of the multidimensional classification probability field by means of a weighted inverse distance average when the number of samples is limited. Nevertheless, comparison with other classifiers revealed that the VEOR algorithm is within the 3-5 most robust techniques for large (i.e., \(>\)=1000 samples) synthetic datasets with low or moderate levels of noise and class imbalance. More importantly, it performed well for the daytime cloud masking over water surfaces using MODIS spectral measurements (ACC = 0.888, \(\kappa\) = 0.748), where it was better than the reference MODIS cloud mask collection 6 product (ACC = 0.881, \(\kappa\) = 0.727), but slightly worse than the random forest (ACC = 0.889, \(\kappa\) = 0.747), neural net (ACC = 0.890, \(\kappa\) = 0.752) and AdaBoost (ACC = 0.891, \(\kappa\) = 0.753) classifiers. An analogous experiment over ice/snow revealed that the VEOR algorithm is again more accurate (ACC = 0.809, \(\kappa\) = 0.611) than the MODIS cloud mask (ACC = 0.671, \(\kappa\) = 0.317) and less accurate than the AdaBoost (ACC = 0.816, \(\kappa\) = 0.629), random forest (ACC = 0.818, \(\kappa\) = 0.632), Gaussian process (ACC = 0.822, \(\kappa\) = 0.639), RBF SVM (ACC = 0.886, \(\kappa\) = 0.745) and neural net (ACC = 0.890, \(\kappa\) = 0.752) classifiers. Interestingly, the forward feature selection method applied to each classifier resulted in a wide range of optimal combinations of features. This is particularly surprising for the daytime cloud discrimination over water surfaces, where different combinations of input features led to almost the same results. Within a more difficult experiment with daytime cloud discrimination over ice/snow, the feature selection was not effective for classifiers featuring limited classification skills over ice/snow (i.e., Naive Bayes method). Finally, it has to be emphasized that the acquired results refer only to low satellite zenith angles as the AQUA MODIS collocations with the CALIOP and CPR profiles are only available close to the satellite nadir. The performed experiments revealed that the decision tree, linear SVM and Naive Bayes produced variable results for the synthetic datasets, provided that some internal assumptions were met or not. The Gaussian process and RBF SVM (based on the libsvm library) classifiers were found to be accurate even for small datasets with a significant noise level. However, their complexity and current implementation within the scikit-learn library do not allow for their application to large EO datasets. Ultimately, the VEOR algorithm features some unique characteristics amongst analyzed classifiers, such as:  
* numerically efficient probabilistic classification of large, multidimensional datasets;
* an internal feature selection method that allows the minimal feature subset required for optimal classification results to be identified;
* the ranking of classification skills for analyzed feature combinations according to [PERSON]'s kappa coefficient;
* intuitive interpretation of derived optimal algorithm settings (including selected features);
* easily expandable LUV whenever new data have to be added to a training dataset.