## 4 Methodology
### Visualization  
To further analyze the obtained results, focusing, in particular, on the descent of accuracy in some combinations of training and test sets, we apply a t-SNE dimensionality reduction approach [10] based on its predecessor, the SNE method [69]. It allows us to produce 2-dimensional visualizations, showing the distribution of samples of a given class originating from various datasets. This technique effectively reduces data complexity while preserving the data points' local structure. It converts the high-dimensional Euclidean distances between data points into conditional probabilities representing similarities. The algorithm then performs a low-dimensional embedding and finally places similar points closer together, locating dissimilar points further apart.  
The t-SNE dimensionality reduction technique belongs to a group of neighborhood-preserving projections. It means that the local vicinities of the input and output data points are analyzed to preserve them. t-SNE can also be characterized as a probabilistic approach because it constructs probability distributions describing the local neighborhoods of the data points so that the distributions show the probability of a given data being in the neighborhood of the currently considered data point.  
The t-SNE method improves the classical Stochastic Neighbor Embedding (SNE) approach. The original SNE algorithm uses the Gaussian probability distributions in both--input and output spaces. It preserves the neighborhood from the input data space in the output data space by minimizing the Kullback--Leibler divergence between the probability distributions. The extension introduced in t-SNE solves a significant difficulty associated with the standard SNE technique. Namely, SNE is vulnerable to the so-called \"crowding problem\" originating from a distortion in the low-dimensional manifold approximation in the output space. The distortion appears when a non-2-dimensional manifold is constructed due to embedding malfunction, and SNE cannot map the considerable dissimilarities correctly. Consequently, certain inaccuracies occur on the visualization display, typically gathering the points in the central part of the display. The \"crowding problem\" is discussed in detail in the work [10]. However, it has also been considered and analyzed in the paper [70].  
In order to overcome the \"crowding problem\", in [10], the Gaussian probability distribution was replaced by the Student's probabilistic t-distribution with one degree of freedom in the output data space. By doing so, they achieved an additional advantage besides the \"crowding problem\" limiting, accelerating the optimization process, i.e., the minimizations of the Kullback--Leibler divergences between the probability distributions in the respective local neighborhoods.  
The ability of the t-SNE method to help observe data properties initially represented by samples in multidimensional space. A two-dimensional t-SNE plot of a dataset with several classes visualizes how samples relate to one another based on their high-dimensional features. Clusters on the plot represent groups of similar samples, meaning that points close together are more alike in the original feature space. When clusters are tightly packed and well-separated, it suggests that the corresponding classes are distinct and easily separable. Conversely, if clusters overlap or appear diffuse, this may indicate similarity between classes or ambiguity in the underlying features, potentially reflecting challengesin classification. This is also the case of network traffic features, where it is successfully applied to investigate the properties of classes distributions in traffic feature space [71; 72; 73; 74; 75]. Our experiments use it to analyze the intra-class variability of samples from different datasets by reducing the 24-dimensional feature space into its two-dimensional equivalent.