Multiclass Oversampling via Optimum-Path Forest for Tree Species Classification from Street-view Perspectives-Reference-Cited by-同舟云学术

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Multiclass Oversampling via Optimum-Path Forest for Tree Species Classification from Street-view Perspectives

Published:2022-10-24 Issue: Volume: Page:
ISSN:
Container-title:2022 35th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
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Author:

Jodas Danilo Samuel¹,Passos Leandro Aparecido²,Del Nero Velasco Giuliana³,Longo Mariana Hortelani Carneseca³,Machado Aline Ribeiro³,Papa Joao Paulo¹

Affiliation:

1. São Paulo State University,Department of Computing,Bauru-SP,Brazil,17033-360

2. University of Wolverhampton,CMI Lab, School of Engineering and Informatics,Wolverhampton,England, United Kingdom

3. Institute for Technological Research, University of São Paulo,São Paulo-SP,Brazil,05508-901

Funder

Engineering and Physical Sciences Research Council

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9991741/9991742/09991757.pdf?arnumber=9991757

Reference33 articles.

1. Conditional Wasserstein GAN-based oversampling of tabular data for imbalanced learning

2. DBIG-US: A two-stage under-sampling algorithm to face the class imbalance problem

3. SMOTE: Synthetic Minority Over-sampling Technique

4. Fine-tuning Generative Adversarial Networks using Metaheuristics

5. Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

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1. A comprehensive study among distance measures on supervised optimum-path forest classification;Applied Soft Computing;2024-10

2. Urban tree failure probability prediction based on dendrometric aspects and machine learning models;Computers, Environment and Urban Systems;2024-03

3. OPFsemble: An Ensemble Pruning Approach via Optimum-Path Forest;2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP);2023-06-27

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