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...

-Imbalanced data set problem occurs in classification, where the number of instances of one class is much lower than the instances of the other classes. The main challenge in imbalance problem is that the small classes are often more useful, but standard classifiers tend to be weighed down by the huge classes and ignore the tiny ones. In machine learning the imbalanced datasets has become a cri...

Abstract The presence of imbalanced classes is more and common in practical applications it known to heavily compromise the learning process. In this paper we propose a new method aimed at addressing issue binary supervised classification. Re-balancing class sizes has turned out be fruitful strategy overcome problem. Our proposal performs re-balancing through matrix sketching. Matrix sketching ...

As growing numbers of real world applications involve imbalanced class distribution or unequal costs for misclassification errors in different classes, learning from imbalanced class distribution is considered to be one of the most challenging issues in data mining research. This study empirically investigates the sensitivity of bagging predictors with respect to 12 algorithms and 9 levels of c...

fuzzy rule-based classification system (frbcs) is a popular machine learning technique for classification purposes. one of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. however many cases the minority classes are more important than the majority ones. in this paper, we have extended ...

In Machine Learning, a data set is imbalanced when the class proportions are highly skewed. Imbalanced data sets arise routinely in many application domains and pose a challenge to traditional classifiers. We propose a new approach to building ensembles of classifiers for two-class imbalanced data sets, called Random Balance. Each member of the Random Balance ensemble is trained with data sampl...

In many data mining applications the imbalanced learning problem is becoming ubiquitous nowadays. When the data sets have an unequal distribution of samples among classes, then these data sets are known as imbalanced data sets. When such highly imbalanced data sets are given to any classifier, then classifier may misclassify the rare samples from the minority class. To deal with such type of im...

0950-7051/$ see front matter 2013 Elsevier B.V. A http://dx.doi.org/10.1016/j.knosys.2013.01.018 ⇑ Corresponding author. Tel.: +34 953 213016; fax: E-mail addresses: [email protected] (A. ugr.es (V. López), [email protected] (M. Galar Jesus), [email protected] (F. Herrera). The imbalanced class problem is related to the real-world application of classification in engineering....

Abstract—Although learning on non-stationary data and imbalanced data have been extensively studied in the literature separately, however little work has been done to tackle the imbalanced issue on nonstationary data stream as the joint probability distribution between the data and classes changes with time and may results skewed class distribution. Especially in airlines delay detection, data ...