Background The goal of class prediction studies is to develop rules to accurately predict the
class membership of new samples. The rules are derived using the values of the variables …

A class-imbalanced classifier is a decision rule to predict the class membership of new
samples from an available data set where the class sizes differ considerably. When the class …

Background Feature selection in class-imbalance learning has gained increasing attention
in recent years due to the massive growth of high-dimensional class-imbalanced data …

Background Classification using class-imbalanced data is biased in favor of the majority
class. The bias is even larger for high-dimensional data, where the number of variables …

As witnessed by a vast corpus of literature, dimensionality reduction is a fundamental step
for biomedical data analysis. Indeed, in this domain, there is often the need for coping with a …

Feature selection and classification of imbalanced data sets are two of the most interesting
machine learning challenges, attracting a growing attention from both, industry and …

Many classification problems must deal with imbalanced datasets where one class–the
majority class–outnumbers the other classes. Standard classification methods do not …

Current research on imbalanced data recognises that class imbalance is aggravated by
other data intrinsic characteristics, among which class overlap stands out as one of the most …

Binary classifiers are routinely evaluated with performance measures such as sensitivity and
specificity, and performance is frequently illustrated with Receiver Operating Characteristics …

Background We consider the problem of designing a study to develop a predictive classifier
from high dimensional data. A common study design is to split the sample into a training set …