We propose to evaluate the quality of decision trees grown on imbalanced datasets with a splitting criterion based on an asymmetric entropy measure. To deal with the class imbalance problem in machine learning, especially with decision trees, different authors proposed such asymmetric splitting criteria. After the tree is grown a decision rule has to be assigned to each leaf. The classical Baye...

Training classifiers with datasets which suffer of imbalanced class distributions is an important problem in data mining. This issue occurs when the number of examples representing the class of interest 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. We shortly review the many issues in mach...

There is an increasing interest in application of Evolutionary Algorithms to induce classification rules. This hybrid approach can aid in areas that classical methods to rule induction have not been completely successful. One example is the induction of classification rules in imbalanced domains. Imbalanced data occur when some classes heavily outnumbers other classes. Frequently, classical Mac...

Despite the large amount of research effort dedicated to adapting boosting for imbalanced classification, boosting methods are yet to be satisfactorily immune to class imbalance, especially for multi-class problems, due to the long-standing reliance on expensive cost set tuning. We show that the assignment of weights to the component classifiers of a boosted ensemble can be thought of as a game...

The classical Otsu method is a common tool in document image binarization. Often, two classes, text and background, are imbalanced, which means that the assumption of not met. In this work, we considered imbalanced pixel classes background text: weights different, but variances same. We experimentally demonstrated employment criterion takes into account imbalance classes' weights, allows attain...

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 ...

Many environmental data are inherently imbalanced, with some majority land use and land cover types dominating over rare ones. In cultivated ecosystems minority classes are often the target as they might indicate a beginning land use change. Most standard classifiers perform best on a balanced distribution of classes, and fail to detect minority classes. We used the synthetic minority oversampl...

Medical datasets are often highly imbalanced with overrepresentation of common medical problems and a paucity of data from rare conditions. We propose simulation of pathology in images to overcome the above limitations. Using chest X-rays as a model medical image, we implement a generative adversarial network (GAN) to create artificial images based upon a modest sized labeled dataset. We employ...

Network traffic data is huge, varying and imbalanced because various classes are not equally distributed. Machine learning (ML) algorithms for traffic analysis uses the samples from this data to recommend the actions to be taken by the network administrators as well as training. Due to imbalances in dataset, it is difficult to train machine learning algorithms for traffic analysis and these may...

Abstract An unsolved problem in the digital mapping of categorical soil variables and types is imbalanced number observations, which leads to reduced accuracy loss minority class (the with a significantly lower observations compared other classes) final map. So far, synthetic over‐ under‐sampling techniques have been explored science; however, more efficient approaches that do not drawbacks the...