Classification of data is difficult if the data is imbalanced and classes are overlapping. In recent years, more research has started to focus on classification of imbalanced data since real world data is often skewed. Traditional methods are more successful with classifying the class that has the most samples (majority class) compared to the other classes (minority classes). For the classifica...

Classification with imbalanced data-sets supposes a new challenge for researches in the framework of data mining. This problem appears when the number of examples that represents one of the classes of the data-set (usually the concept of interest) is much lower than that of the other classes. In this manner, the learning model must be adapted to this situation, which is very common in real appl...

The class imbalance problem in classification has been recognized as a significant research problem in recent years and a number of methods have been introduced to improve classification results. Rebalancing class distributions (such as over-sampling or under-sampling of learning datasets) has been popular due to its ease of implementation and relatively good performance. For the Support Vector...

0950-7051/$ see front matter 2011 Elsevier B.V. A doi:10.1016/j.knosys.2011.06.013 ⇑ Corresponding author. E-mail addresses: [email protected] (V. García), s [email protected] (R.A. Mollineda). The present paper investigates the influence of both the imbalance ratio and the classifier on the performance of several resampling strategies to deal with imbalanced data sets. The study focuses on evaluat...

In recent years, the algorithms of learning to rank have been proposed by researchers. However, in information retrieval, instances of ranks are imbalanced. After the instances of ranks are composed to pairs, the pairs of ranks are imbalanced too. In this paper, a cost-sensitive risk minimum model of pairwise learning to rank imbalanced data sets is proposed. Following this model, the algorithm...

In pattern recognition and data mining a data set is named skewed or imbalanced if it contains a large number of objects of certain type and a very small number of objects of the opposite type. The imbalance in data sets represents a challenging problem for most classification methods, this is because the generalization power achieved for classic classifiers is not good for skewed data sets. Ma...

In the field of classification problems, we often encounter classes with a very different percentage of patterns between them, classes with a high pattern percentage and classes with a low pattern percentage. These problems receive the name of “classification problemswith imbalanced data-sets”. In this paperwe study the behaviour of fuzzy rule based classification systems in the framework of im...

Today’s most of the research interest is in the application of evolutionary algorithms. One of the examples is classification rules in imbalanced domains. The problem of Imbalanced data sets plays a major challenge in data mining community. In imbalanced data sets, the number of instances of one class is much higher than the others, and the class of fewer representatives is of more interest fro...

Some real world data mining applications present imbalanced or skewed class distributions. In these domains, the underrepresented classes are often the ones we are more interested in. However, most learning algorithms are not able to induce meaningful classifiers in some imbalanced domains. One reason for this poor performance is that learning algorithms tend to focus in abundant classes to max...