## 7 Discussion  
The experiments with the classification of synthetic and satellite datasets revealed that the VEOR algorithm provides similar or better classification results compared with other widely used machine learning algorithms with fine-tuned hyper-parameters and optimized input features. In this respect, it performed among the top 3-5 most accurate algorithms for synthetic datasets with low and moderate levels of noise and class imbalance, as well as for cloud detection using MODIS/CALIOP/CPR collocations. For small, noisy (30% of mislabeling) and imbalanced (30%/70%) datasets, the VEOR provided moderate  
Figure 10: Comparison of single-core algorithm classification processing times for selected classifiers. Figure 9: Comparison of single-core algorithm training processing times for selected classifiers. results comparable to the random forest classifier. This is due to the fact that a significantly disturbed classification probability field is difficult to reconstruct by means of a simple inverse distance weighted average, and thus has to be modeled. Therefore, the classifiers that involve more advanced modeling approaches, such as SVM or Gaussian process, perform better for such datasets but at the price of large computational costs. Nevertheless, classification of an extensive volume of remote sensing data requires a method that is both numerically efficient and accurate. Such a combination is reached by the neural network classifier, which provides accurate classification results, but its internal configuration is difficult to interpret. In this respect, the VEOR method ranks single features and combinations of features according to [PERSON]'s kappa score. This greatly facilitates the understanding of the feature selection process. Conceptually simple methods such as nearest neighbor, Naive Bayes or QDA do not fit well with all datasets, especially with multispectral MODIS measurements over ice/snow surfaces. Interestingly, for some difficult synthetic datasets, these methods perform well in comparison to the most robust classifiers. This may indicate that if some assumptions behind such methods are met (e.g., Gaussian distribution describing each class and/or linear separability of classes), then they are able to provide accurate classification results with great numerical efficiency. Another type of classifier is the so-called \"weak learners\", such as the decision tree algorithm, which generally do not feature good classification skills (see Figures 5-8) but are extremely fast and simple to apply. Therefore, they can be executed multiple or hundreds of times with different settings and their stochastic results can be further post-processed to derive an accurate ensemble classifier such as the random forest or AdaBoost. The latter algorithms were found to be robust especially for the classification of multispectral MODIS data and synthetic datasets with a low level of noise and small class imbalance. The reiteration of a stochastic classifier several times with the same input data increases the representability of the classification results, as some initial settings within an algorithm optimization are set by a random guess. Consequently, without repetition, the stochastic classifiers (e.g., decision tree) may provide significantly different results for the same input data. Another source of algorithm instability may be caused by a convergence problem when an iterative algorithm optimization procedure fails due to an unrealistic assumption (e.g., linearity in the case of the linear SVM) or a small number of iterations. Apart from the classifier itself, an additional source of variability in the performed experiments originates from the stochastic wrapper procedures to optimize the hyper-parameters (HalvingGridSearchCV--scikit-learn library routine) and to select the most optimal features (SequentialFeatureSelector--scikit-learn library routine).