The imbalanced learning issue is related to the performance of learning algorithms in the
presence of asymmetrical class distribution. Due to the complex characteristics of …

Many real-world data-mining applications involve obtaining predictive models using
datasets with strongly imbalanced distributions of the target variable. Frequently, the least …

Integrating Artificial Intelligence (AI) techniques with remote sensing holds great potential for
revolutionizing data analysis and applications in many domains of Earth sciences. This …

Learning classifiers from imbalanced or skewed datasets is an important topic, arising very
often in practice in classification problems. In such problems, almost all the instances are …

The purpose of this paper is to analyze the imbalanced learning task in the multilabel
scenario, aiming to accomplish two different goals. The first one is to present specialized …

The class imbalance problem has been recognized in many practical domains and a hot
topic of machine learning in recent years. In such a problem, almost all the examples are …

Learning from imbalanced data is a problem which arises in many real-world scenarios, so
does the need to build classifiers able to predict more than one class label simultaneously …

In this paper, a novel inverse random under sampling (IRUS) method is proposed for the
class imbalance problem. The main idea is to severely under sample the majority class thus …

Abstract 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 …

Corporate bankruptcy prediction is very important for creditors and investors. Most literature
improves performance of prediction models by develo** and optimizing the quantitative …