ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION
The extremely skewed data in artificial intelligence, machine learning, and data mining cases are often given misleading results. It is caused because machine learning algorithms are designated to work best with balanced data. However, we often meet with imbalanced data in the real situation. To han...
Guardado en:
Autores principales: | Ariani Indrawati, Hendro Subagyo, Andre Sihombing, Wagiyah Wagiyah, Sjaeful Afandi |
---|---|
Formato: | article |
Lenguaje: | EN ID |
Publicado: |
Lembaga Ilmu Pengetahuan Indonesia
2020
|
Materias: | |
Acceso en línea: | https://doaj.org/article/bf6a90b54cc34ae89e9eee9d4ad072ff |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Point Cloud Resampling by Simulating Electric Charges on Metallic Surfaces
por: Kyoungmin Han, et al.
Publicado: (2021) -
Exploratory data analysis in the context of data mining and resampling.
por: Chong Ho Yu
Publicado: (2010) -
Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems
por: Tran Khanh Dang, et al.
Publicado: (2021) -
Comparison of Resampling Techniques for Imbalanced Datasets in Machine Learning: Application to Epileptogenic Zone Localization From Interictal Intracranial EEG Recordings in Patients With Focal Epilepsy
por: Giulia Varotto, et al.
Publicado: (2021) -
TWO-WAY METRIC LEARNING WITH MAJORITY AND MINORITY SUBSETS FOR CLASSIFICATION OF LARGE EXTREMELY IMBALANCED FACE DATASET
por: Ashu Kaushik, et al.
Publicado: (2021)