A novel ensemble method for classifying imbalanced data
By Zhongbin Sun, Qinbao Song, Xiaoyan Zhu, Heli Sun, Baowen Xu, Yuming Zhou. Pattern Recognition, 2015
399 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/e97da541c82aac42b85190d315800fd94767e0ee
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Qi Tian
- Shiliang Zhang
- Rohitash Chandra
- Harsurinder Kaur
- Wen Gao
- Azal Ahmad Khan
- Shiqing Zhang
- Omkar Chaudhari
In Electrical and Electronic Engineering
- Qi Tian
- Rohitash Chandra
- Harsurinder Kaur
- Wen Gao
- Azal Ahmad Khan
- Omkar Chaudhari
- Husanbir Singh Pannu
- Avleen Kaur Malhi
Citations in the corpus
- A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation (2023, methodology): “Sun et al. [270] proposed a novel ensemble method that converts an imbalanced dataset into several balanced datasets.”
- Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition (2018, background): “For more details about the six ensemble rules, refer to [62] and [63].”
- A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, methodology): “Sun et al. (2015) proposed a novel ensemble method in which imbalanced dataset is divided into creating many balanced dataset.”