OpenAlex | SMOTE-BD: An Exact and Scalable Oversampling Method for Imbalanced Classification in Big Data

SMOTE-BD: An Exact and Scalable Oversampling Method for Imbalanced Classification in Big Data

Work

Year: 2018

Type: article

Abstract: The volume of data in today's applications has meant a change in the way Machine Learning issues are addressed. Indeed, the Big Data scenario involves scalability constraints that can only be achieved... more

Source: Journal of Computer Science and Technology

Authors María José Basgall, Waldo Hasperué, Marcelo Naiouf, Alberto Fernández, Francisco Herrera

Institutions Consejo Nacional de Investigaciones Científicas y Técnicas, Universidad Nacional de La Plata, Instituto Andaluz de Ciencias de la Tierra, Universidad de Granada

Cites: 12

Cited by: 29

Related to: 10

FWCI: 2.408

Citation percentile (by year/subfield): 86.46

Topic: Imbalanced Data Classification Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Sustainable Development Goal Industry, innovation and infrastructure

Open Access status: diamond