Unbalanced data set, a problem often found in real world application, can cause seriously
negative effect on classification performance of machine learning algorithms. There have …

The past decade has seen a significant interest on the problem of inducing decision trees
that take account of costs of misclassification and costs of acquiring the features used for …

Highly imbalanced data typically make accurate predictions difficult. Unfortunately, software
defect datasets tend to have fewer defective modules than non-defective modules. Synthetic …

An approach to the construction of classifiers from imbalanced datasets is described. A
dataset is imbalanced if the classification categories are not approximately equally …

In machine learning problems, differences in prior class probabilities--or class imbalances--
have been reported to hinder the performance of some standard classifiers, such as …

Adding examples of the majority class to the training set can have a detrimental e ect on the
learner's behavior: noisy or otherwise unreliable examples from the majority class can …

Decision trees have become one of the most powerful and popular approaches in
knowledge discovery and data mining; it is the science of exploring large and complex …

Methods for voting classification algorithms, such as Bagging and AdaBoost, have been
shown to be very successful in improving the accuracy of certain classifiers for artificial and …

The class imbalanced problem occurs in various disciplines when one of target classes has
a tiny number of instances comparing to other classes. A typical classifier normally ignores …

This paper takes a new look at two sampling schemes commonly used to adapt machine
learning algorithms to imbalanced classes and misclassification costs. It uses a performance …