## 3 Public Datasets for Scene Understanding Methods
### Solutions to Handle Dataset Imbalance
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline \multicolumn{2}{c}{**Name**} & **Approach** \\ \hline \multirow{4}{*}{Random Undersampling} & Randomly selects a subset of the majority class and discards the rest. \\ \cline{2-3}  & Tomek Links & Removes samples from the majority class that are close to samples in the minority class. \\ \cline{2-3}  & Near Miss & Selects a subset of the majority class such that the samples are close to the decision boundary between the minority and majority classes. \\ \cline{2-3}  & One-Sided Selection & Selects a subset of the majority class such that the samples are more dissimilar to the minority class than the majority class samples that are not selected. \\ \cline{2-3}  & Cluster Centroids & Creates clusters of the majority class and replaces each cluster with its centroid. \\ \cline{2-3}  & Condensed Nearest Neighbor & Selects a subset of the majority class such that each sample in the subset has at least one majority class sample that is not its nearest neighbor. \\ \cline{2-3}  & Edited Nearest Neighbor & Iteratively removes samples from the majority class such that the remaining samples are all “agreeable” with the minority class. Agreeable examples are those that are not misclassified by their nearest neighbor classifier. \\ \hline \multirow{4}{*}{Random Oversampling} & Randomly selects samples from the minority class and duplicates them in the dataset. \\ \cline{2-3}  & Synthetic Minority & Generates synthetic samples for the minority class by interpolating between existing samples. \\ \cline{2-3}  & Oversampling & Similar to SMOTE, but generates more synthetic samples for the minority class in regions where the classifier is less accurate. \\ \cline{2-3}  & Borderline Oversampling & Generates synthetic samples for the minority class that are close to the decision boundary between the minority and majority classes. \\ \cline{2-3}  & Backpropagation-Based & Generates synthetic samples for the minority class using back-propagation to learn a transformation from the majority class to the minority class. \\ \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Most commonly used undersampling and oversampling methods in standard classification tasks [90; 91; 95; 96]. Figure 8: Graphical representation of the two principles of under- and oversampling for imbalanced training data handling in machine learning applications. inaccurate results. Random undersampling of the majority class (Figure 8, left) is easy to implement and fast to execute. At the same time, it is associated with an uncontrollable risk of information loss. To address this limitation, more advanced techniques have been developed that leverage meta-information, like the distance between two samples, neighborhood knowledge, clustering, and even evolutionary algorithms, like simulating bird flocking behavior [96]. Generating more instances of the underrepresented class through **oversampling** (Figure 8, right) does not lead to information loss, which makes it a popular technique for balancing imbalanced datasets [97]. Applying oversampling techniques to the minority class can enhance the performance of a classifier on that particular class. The downside of oversampling is opaque. By replicating synthetic instances of the original dataset, the likelihood of creating random noise and causing class-overfitting increases. Overfitting, in particular, occurs when classifiers produce multiple clauses in a rule for multiple copies of the same example, which causes the rule to become overly specific. Although the training accuracy will be high in this scenario, the classification performance on the unseen testing data is generally far worse [91,96]. Oversampling for multi-class datasets potentially produces large amounts of data, which is storage-intensive and time-consuming to train with. It is important to note that oversampling before splitting the data into training/testing sets can lead to identical copies in both sets. This causes overfitting and poor generalization.