The Use of Hellinger Distance Undersampling Model to Improve the Classification of Disease Class in Imbalanced Medical Datasets
Imbalanced class distribution in the medical dataset is a challenging task that hinders classifying disease correctly. It emerges when the number of healthy class instances being much larger than the disease class instances. To solve this problem, we proposed undersampling the healthy class instance...
Saved in:
| Main Authors: | Zina Z. R. Al-Shamaa, Sefer Kurnaz, Adil Deniz Duru, Nadia Peppa, Alex H. Mirnezami, Zaed Z. R. Hamady |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Applied Bionics and Biomechanics |
| Online Access: | http://dx.doi.org/10.1155/2020/8824625 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning
by: Haiou Qin
Published: (2025-01-01) -
In memoriam Bert Hellinger (1925-2019)
by: Francisco Gómez Gómez
Published: (2020-09-01) -
Optimizing Performance of AdaBoost Algorithm through Undersampling and Hyperparameter Tuning on CICIoT 2023 Dataset
by: Sahrul Fahrezi Fahrezi, et al.
Published: (2024-11-01) -
Impact of imbalanced features on large datasets
by: Waleed Albattah, et al.
Published: (2025-03-01) -
Drilling Condition Identification Method for Imbalanced Datasets
by: Yibing Yu, et al.
Published: (2025-03-01)