Using a Many-Objective Optimization Algorithm to Select Sampling Approaches for Imbalanced Datasets

Miranda, Pericles BC; Morais, Romero FAB; Silva, Ricardo MA

Using a Many-Objective Optimization Algorithm to Select Sampling Approaches for Imbalanced Datasets

conference contribution

posted on 2024-06-03, 06:49 authored by Pericles BC Miranda, Romero FAB Morais, Ricardo MA Silva

Using a Many-Objective Optimization Algorithm to Select Sampling Approaches for Imbalanced Datasets

History

Pagination

2324-2330

Location

Rio de Janeiro, Brazil

Start date

2018-07-08

End date

2018-07-13

Language

English

Author URL

http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000451175500297&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a045e4b2bb1f2b747c68c720ec8913b7

Publication classification

E1 Full written paper - refereed

Title of proceedings

CEC 2018: Proceedings of the Congress on Evolutionary Computation 2018

Event

IEEE on Evolutionary Computation. Congress (2018 : Rio de Janeiro, Brazil)

Publisher

IEEE

Place of publication

Piscataway, N.J.

Series

IEEE Congress on Evolutionary Computation

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Keywords

Computer Science Computer Science, Artificial Intelligence Computer Science, Theory & Methods DESIGN Engineering Engineering, Electrical & Electronic Imbalanced Datasets Many-Objective Optimization Sampling Algorithms Science & Technology Technology

Using a Many-Objective Optimization Algorithm to Select Sampling Approaches for Imbalanced Datasets

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