A New Approach for Developing Segmentation Algorithms for Strongly Imbalanced Data
During the past two decades, the problem of how to develop efficient segmentation algorithms for dealing with strongly imbalanced data has been drawing much attention of researchers and practitioners in the field of data mining. A typical approach for this difficult problem is represented by a rando...
Main Authors: | Kazuki Fujiwara, Maiko Shigeno, Ushio Sumita |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8737955/ |
Similar Items
-
Selective Ensemble Learning Algorithm for Imbalanced Dataset
by: Chen, Y.-C, et al.
Published: (2023) -
Uncertainty Based Under-Sampling for Learning Naive Bayes Classifiers Under Imbalanced Data Sets
by: Christos K. Aridas, et al.
Published: (2020-01-01) -
Using Cost-Sensitive Learning and Feature Selection Algorithms to Improve the Performance of Imbalanced Classification
by: Fang Feng, et al.
Published: (2020-01-01) -
SCUT-DS: Methodologies for Learning in Imbalanced Data Streams
by: Olaitan, Olubukola
Published: (2018) -
Imbalanced SVM‐Based Anomaly Detection Algorithm for Imbalanced Training Datasets
by: GuiPing Wang, et al.
Published: (2017-10-01)