## 4 Experimental Settings
### Algorithm Selectors  
To train Algorithm selectors, we extracted feature representations from input images using a pretrained AlexNet [38] model. Specifically, we used the intermediate activations from five convolutional layers of the network as a compact image descriptor. This approach enables the capture of meaningful visual patterns at various levels of abstraction, while keeping the feature dimensionality within a practical range. All input images were resized to \(128\times 128\) before being passed through the feature-extraction pipeline. For each image, we computed the mean activation values across feature maps from the first, fourth, seventh, ninth, and eleventh convolutional layers. The resulting values were concatenated into a single feature vector. Each vector was then labeled with the identity of the best-performing classification Algorithm for the corresponding image, determined based on validation accuracy. For the evaluation of the methodology, we used nine different shallow Algorithm selectors: ExtraTreesClassifier [39], SVM [40], DecisionTreeClassifier [41], KNeighborsClassifier [42], GradientBoostingClassifier [43], SGDClassifier [44], LogisticRegression [45], \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm Name** & **Specificity** & **Comments** \\ \hline \multirow{2}{*}{DPN92 [33]} & Combination of ResNet & Trained on ImageNet-1k \\  & and DenseNet & in MXNet \\ \hline \multirow{2}{*}{ResNet50 [34]} & Multiple blocks of convolution layers & \\  & Average pooling, max pool & \\ \hline \multirow{2}{*}{DenseNet121 [35]} & Global Average Pooling, Extracts & \\  & features form all feature maps \\ \hline \multirow{2}{*}{VGG11 [36]} & 8 Convolution layers & \\  & Max pool and three linear layers & \\ \hline \multirow{2}{*}{SqueezeNet [37]} & \multirow{2}{*}{All kernels are \(1\times 1\) size} & Combination of ResNet and \\  & & Reduced-size filters \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of the individual network models used for the classification task. RandomForestClassifier [46], and MLPClassifier [47]. We represent them as \(m\in\mathbb{M}\), where \(\mathbb{M}=\{m_{1},\dots,m_{k}\}\) is the set of all Algorithm selectors. Each classifier is trained in an identical manner described specifically for each experiment and learning the mapping \(\mathcal{M}:\mathbb{I}_{S}\rightarrow\mathbb{L}_{S}\). In this context, meta-learning refers to the process where the Algorithm selector learns to predict which component Algorithm will perform best for a given input. This approach falls under the broader meta-learning framework, where knowledge is transferred across learning episodes, allowing the system to learn from previous Algorithm performances to improve future selections. The Algorithm selector essentially operates at a meta level, learning patterns about when different Algorithms perform well rather than solving the original classification task directly. However, because the task at hand is classification, we have to prepare the training data in a specific manner. This is to address the problem of multiple correct answers during the learning of the selector. During the learning process of the selector, the dataset \(\mathbb{D}_{S}\) samples \(x^{i}\) are to be mapped to a unique label \(l^{i}\). However, due to complementary performance not being totally complementary, many samples \(x^{i}\) are such that they are correctly classified by more than one Algorithm, creating the mapping \(\{x_{i},[a_{i}\dots a_{j}]\}\). Therefore, during the training process one has to decide which target Algorithm will be fed to the classifier as training sample. We explore three possible strategies. The first strategy simply creates the sample by providing the target label from the classifier with the highest confidence score, referred to as \(conf\). The second approach assign the target label randomly and is referred to as \(rand\). The last strategy is basically to create as many samples as there are target labels and then include them into the final dataset with equal probability.