## 2 The Challenges of Image Classification
### Imbalanced Distribution of Classes  
This common classification problem is related to an imbalanced class distribution, where one class occurs more frequently than other(s). Consequently, different classifiers tend to overestimate the frequency of the dominant class. This improves some of the classification quality statistics (e.g., accuracy) but leads to a severe misclassification of the rare classes. This may turn the classification task based on input features into a class assignment based on a priori information about class frequency distribution. To mitigate this problem, two approaches are possible. The first is to balance the training dataset by oversampling the rare classes or undersampling the prevalent classes. Some more sophisticated methods, such as the Synthetic Minority Oversampling Technique (SMOTE) [55], may be used to combine both sampling techniques. The second approach to the class imbalance problem involves different corrections applied to classifiers, such as cost-sensitive learning to more severely penalize the misclassification of rare classes. This can be implemented by the utilization of specific quality measures during classifier optimization, such as the Hansen-Kuipers Skill score [56], F-score [57], G-mean [58], the area under the Receiver Operating Characteristics (ROC) curve [59], Cohen kappa coefficient [60] and average mutual information [61], which are less sensitive to the class imbalance problem as opposed to overall classification accuracy. Comprehensive reviews of the deficiencies of common classification algorithms in the presence of the imbalanced distribution of classes were reported by [41; 62; 63]. In this section, only the most important aspects related to the class imbalance problem are discussed.  
In the case of the KNN classifier, it is quite intuitive that the neighborhood of a pixel within a feature space more likely consists of a prevalent class than infrequent classes. Thus, infrequent classes are more likely to be incorrectly classified. This effect can be partially diminished by balancing the class distribution within the training dataset. In the case of the decision tree classifier, an imbalanced training dataset results in many tests (branches within the trees) to separate infrequent classes from prevalent classes. Usually, too few tests are implemented to correctly delineate small classes or they are removed during the tree pruning procedure due to not being significant. Some solutions to this problem involve adjustment of the scores generated by each test [64] or the involvement of more advanced tree pruning techniques [65]. SVM classifiers are also reported to be sensitive to the class imbalance problem [66]. In such a case, the class-separating hyperplanes become more skewed towards the prevalent class as the number of support vectors is higher for this class. Consequently, the nearest neighborhood of a test sample (especially when it is located near the separating hyperplane) is likely to be dominated by the support vectors of the dominant class, which may lead to a misclassification of the infrequent class. To overcome this problem, [66] proposed the class boundary alignment algorithm, which adjusts the class boundary either by transforming the kernel function, where the training data can be represented in a vector space, or by modifying the kernel matrix when the data do not have vector space representation. For the neural network algorithm, the imbalanced distribution of classes alters the weights of connections between neurons during the iterative optimization of a network. Consequently, the classification error decreases for the prevalent class and at the same time increases for the infrequent classes. One of the potential solutions to this problem involves undersampling the prevalent class by eliminating highly correlated cases [67]. In the case of the Naive Bayes classifier, the imbalanced training dataset may lead to an overfitting of the learned parameters [68] and to poor classification performance.