Logistic regression is a standard procedure for real-world classification problems. The challenge of class imbalance arises in two-class problems when the minority observed much less than majority class. This characteristic endemic many domains. Work by Owen has shown that cluster structure among may be specific problem highly imbalanced logistic regression. In this article, we propose novel re...

Learning from class imbalanced datasets poses challenges for many machine learning algorithms. Many real-world domains are, by definition, virtue of having a majority that naturally has more instances than its minority (e.g., genuine bank transactions occur much often fraudulent ones). methods have been proposed to solve the imbalance problem, among most popular being oversampling techniques (s...

Class imbalance data usually suffers from data intrinsic properties beyond that of imbalance alone. The problem is intensified with larger levels of imbalance most commonly found in observational studies. Extreme cases of class imbalance are commonly found in many domains including fraud detection, mammography of cancer and post term births. These rare events are usually the most costly or have...

In this article, a Multi-Objective Memetic Algorithm (MA) for rule learning is proposed. Prediction accuracy and interpretation are two measures that conflict with each other. In this approach, we consider accuracy and interpretation of rules sets. Additionally, individual classifiers face other problems such as huge sizes, high dimensionality and imbalance classes’ distribution data sets. This...

The class imbalance problem has been considered a critical factor for designing and constructing the supervised classifiers. In the case of artificial neural networks, this complexity negatively affects the generalization process on under-represented classes. However, it has also been observed that the decrease in the performance attainable of standard learners is not directly caused by the cla...

This chapter is concerned with the class imbalance problem, which has been recognised as a crucial problem in machine learning and data mining. The problem occurs when there are significantly fewer training instances of one class compared to another class.