The class imbalanced problem can be considered one of the top problem in data mining today, as it is present in many real-world domains such as computer science, epidemiology, finance and so on. This has brought along a growth attention from both academia and industry. In this master thesis a critical study of the nature of the problem, the state-of-art solutions, an explanation of specif measure of performance and a real application of this problem has been carried out. In particular in the first part of the work a discussion about the problem of data imbalanced itself have been presented. We will analyze how the skewed distribution affects standard classification learning algorithms that are generally biased towards majority class.