## 2 Background
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**References** & **Domain of Interest** & **Solution for Small** & **Classification/** & **Methods/Techniques** & **Tools Used** & **Dataset for** \\
**(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** & **(7)** \\ \hline
[46] & Enhancing energy prediction & Virtual sample generation through nonlinear interpolation & Regression & Nonlinear interpolation, EML, non-linear interpolation based virtual sample generation & NA & 50 production data items from Chinese plants between 2011–2013; five input variables \\ \hline
[47] & Exploring new structural materials & Extracting relevant properties from a high dimensional small training dataset & Regression & Kernel Regression-based Learning, Kernel Recursive Least Squares, expert knowledge & C++, dlib, MongDB & \
\begin{tabular}{c} Tensile test specimen and \\ spherical micro samples; the \\ training data of grid points, \\ i.e., fully classified structural \\ materials of 6500 data points; \\ 15 kernel functions \\ \end{tabular} &
\begin{tabular}{c} Kernel \\ spherical micro samples; the \\ training data of grid points, \\ i.e., fully classified structural \\ materials of 6500 data points; \\ 15 kernel functions \\ \end{tabular} \\ \hline
[48] & Infection diagnosis & Aggregating existing measurements to generate required features & Classification & \
\begin{tabular}{c} Decision support \\ system based on \\ ensemble of \\ Decision Trees, \\ k-nearest \\ neighbours, logistic \\ regression, \\ multi-layer \\ perceptron, SVM \\ \end{tabular} & NA &
\begin{tabular}{c} Real dataset of 60 patients for \\ infections; three variables \\ \end{tabular} \\ \hline
[49] & Interactive knowledge extraction based on feature relevance & Interactive visualisation for extracting tacit prior knowledge & Classification, Regression & \begin{tabular}{c} Interactive \\ visualisation (IVs), \\ user model \\ \end{tabular} & \
\begin{tabular}{c} Primary Natural \\ Language Toolkit, \\ Python Rake and \\ KPMiner \\ \end{tabular} &
\begin{tabular}{c} The user’s knowledge about \\ feature relevance from 162 \\ scientific documents; 457 \\ unique keywords that were \\ used as features \\ \end{tabular} \\ \hline
[50] & Medical applications & Surrogate data generated using statistical features of the original dataset & Regression & NN & NA & 56 samples; 5 input parameters \\ \hline
[51] & Marine oil spill detection from images & The ResNet model for extracting the feature maps with the input data & Classification & \
\begin{tabular}{c} Convolutional NN \\ (CNN) \\ \end{tabular} &
\begin{tabular}{c} FCN-GoogLeNet \\ and FNC-ResNet \\ models \\ \end{tabular} & 20 oil spill images \\ \hline
[52] & Oil spill detection from the disease & Generating an oil split detection map image & Classification & \
\begin{tabular}{c} Multiscale \\ conditional \\ adversarial network \\ \end{tabular} & NA &
\begin{tabular}{c} Four oil spill image pairs \\ (size of 256 \(\times\) 256 pixels) \\ \end{tabular} \\ \hline
[53] & Oil spill detection & Generating new examples applying the Mega-Trend Diffusion function, intelligent & Classification & \
\begin{tabular}{c} Unsupervised \\ algorithm SOM \\ \end{tabular} & MATLAB, Libswm &
\begin{tabular}{c} Pictures of real oil spills \\ compared to pictures of fake \\ spills \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. RMSE belongs to the positive values interval, where 0 indicates a perfect fit to the data. Since RMSE can vary in the interval \((0,+\infty)\), some authors [36; 57; 58] propose using normalised RMSE (Equation (2)). Then, a normalised RMSE (NRMSE) number ranges in the interval of [0; 1], where values closer to 0 represent better fitting predictions. \[\text{NRMSE}=\frac{\text{RMSE}}{\text{max}f_{i}-\text{min}f_{i}} \tag{2}\]  
The coefficient of determination (\(R^{2}\)) is used to show how many predicted data points (\(f_{i}\)) meet the regression line (Equation (3) [56]). Its meaning varies in the interval from 0 to 1, where 1 indicates a perfect fit and 0 no fit at all.