It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an example of imbalanced label distribution, because some classes of emotions like disgusted are relatively rare comparing to other labels like happy or sad. In this paper, we propose a da...

-The class imbalanced problem occurs in various disciplines when one of target classes has a small number of instances compare to other classes. A classifier normally ignores or neglects to detect a minority class due to the small number of class instances. It poses a challenge to any classifier as it becomes hard to learn the minority class samples. Most of the oversampling methods may generat...

Transductive graph-based semisupervised learning methods usually build an undirected graph utilizing both labeled and unlabeled samples as vertices. Those methods propagate label information of labeled samples to neighbors through their edges in order to get the predicted labels of unlabeled samples. Most popular semi-supervised learning approaches are sensitive to initial label distribution wh...

The problem of imbalanced training data in supervised methods is currently receiving growing attention. Imbalanced data means that one class is much more represented than the others in the training sample. It has been observed that this situation, which arises in several practical domains, may produce an important deterioration of the classification accuracy, in particular with patterns belongi...

In this thesis we study the classification task in the presence of class imbalanced data. This task arises in many applications when we are interested in the under-represented (minority) classes. Examples of such applications are related to fraud detection, medical diagnosis and monitoring, text categorization, risk management, information retrieval and filtering. Although there exist many stan...

This paper analyzes the effects of distance between classes and training datasets size to XCS classifier system on imbalanced datasets. Our purpose is to answer the question whether the loss of performance incurred by the classifier faced with class imbalance problems stems from the class imbalance per se or it can be explained in some other ways. The experiments from 250 artificial imbalanced ...

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. In such scenarios, the datasets have skewed class distributions while very few data instances are associated with certain classes. The classifiers developed on such datasets tend to favor the majority classes and are biased against the minority class. D...

In order to control the trade-off between sensitivity and specificity of MLP binary classifiers, we extended the Backpropagation algorithm, in batch mode, to incorporate different misclassification costs via separation of the global mean squared error between positive and negative classes. By achieving different solutions in ROC space, our algorithm improved the MLP classifier performance on im...

The problem of imbalanced training sets in supervised pattern recognition methods is receiving growing attention. Imbalanced training sample means that one class is represented by a large number of examples while the other is represented by only a few. It has been observed that this situation, which arises in several practical domains, may produce an important deterioration of the classificatio...

Classifier learning with datasets which suffer from imbalanced class distributions is an important problem in data mining. This issue occurs when the number of examples representing one class is much lower than the ones of the other classes. Its presence in many real-world applications has brought along a growth of attention from researchers. The aim of this work is to shortly review the main i...