Cost-sensitive learning strategies for high-dimensional and imbalanced data: a comparative study.

ABSTRACT: High dimensionality and class imbalance have been largely recognized as important issues in machine learning. A vast amount of literature has indeed investigated suitable approaches to address the multiple challenges that arise when dealing with high-dimensional feature spaces (where each problem instance is described by a large number of features). As well, several learning strategies have been devised to cope with the adverse effects of imbalanced class distributions, which may severely impact on the generalization ability of the induced models. Nevertheless, although both the issues have been largely studied for several years, they have mostly been addressed separately, and their combined effects are yet to be fully understood. Indeed, little research has been so far conducted to invest

SUBMITTER: Pes B 

PROVIDER: S-EPMC8725666 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature