SMO-RF:A machine learning approach by random forest for predicting class imbalancing followed by SMOTE-Reference-Cited by-同舟云学术

SMO-RF:A machine learning approach by random forest for predicting class imbalancing followed by SMOTE

Published:2021-02 Issue: Volume: Page:
ISSN:2214-7853
Container-title:Materials Today: Proceedings
language:en
Short-container-title:Materials Today: Proceedings

Author:

Goyal Ankur,Rathore Likhita,Sharma Avinash

Publisher

Elsevier BV

Subject

General Medicine

Reference20 articles.

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2. Enislay Ramentol, Yailé Caballero, Rafael Bello, Francisco Herrera,” SMOTE-RSB∗: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory”, Knowl Inf Syst DOI 10.1007/s10115-011-0465-6.

3. A novel ensemble classification model based on neural networks and a classifier optimization technique for imbalanced credit risk evaluation”;Shen;Phys. A,2019

4. https://www.hindawi.com/journals/mpe/2015/269856/.

5. https://machinelearningmastery.com/what-is-imbalanced-classification/.

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