Efficient resampling methods for training support vector machines with imbalanced datasets

Efficient resampling methods for training support vector machines with imbalanced datasets

Detalhes bibliográficos
Principais autores:	Batuwita, R, Palade, V
Formato:	Conference item
Publicado em:	2010

Registros relacionados

Class Imbalance Learning Methods for Support Vector Machines
por: Batuwita, R, et al.
Publicado em: (2013)

Adjusted Geometric−mean: A Novel Performance Measure for Imbalanced Bioinformatics Datasets Learning
por: Batuwita, R, et al.
Publicado em: (2012)

FSVM-CIL: Fuzzy Support Vector Machines for Class Imbalance Learning.
por: Batuwita, R, et al.
Publicado em: (2010)

FSVM−CI: Fuzzy Support Vector Machines for Class Imbalance Learning
por: Batuwita, R, et al.
Publicado em: (2010)

Resampling Technique for Imbalanced Class Handling on Educational Dataset
por: Anief Fauzan Rozi, et al.
Publicado em: (2023-05-01)

Resampling imbalanced data for network intrusion detection datasets
por: Sikha Bagui, et al.
Publicado em: (2021-01-01)

Support vector machine for solving imbalanced dataset problem /
por: Ismail Mohd. Khairuddin, 1987-, et al.
Publicado em: (2012)

Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach
por: Ernesto Lee, et al.
Publicado em: (2021-01-01)

Resampling Imbalanced Network Intrusion Datasets to Identify Rare Attacks
por: Sikha Bagui, et al.
Publicado em: (2023-03-01)

Support vector machine for solving imbalanced dataset problem [electronic resource] /
por: Ismail Mohd. Khairuddin, 1987-
Publicado em: (2012)

Ensemble classifier and resampling for imbalanced multiclass learning
por: Sainin, Mohd Shamrie, et al.
Publicado em: (2015)

A stacked ensemble approach with resampling techniques for highly effective fraud detection in imbalanced datasets
por: Idongesit E. Eteng, et al.
Publicado em: (2025-02-01)

Highly Imbalanced Classification of Gout Using Data Resampling and Ensemble Method
por: Xiaonan Si, et al.
Publicado em: (2024-03-01)

Predicting defects in imbalanced data using resampling methods: an empirical investigation
por: Ruchika Malhotra, et al.
Publicado em: (2022-04-01)

Effect of data resampling on feature importance in imbalanced blockchain data: comparison studies of resampling techniques
por: Ismail Alarab, et al.
Publicado em: (2022-06-01)

Fuzzy and smote resampling technique for imbalanced data sets
por: Zorkeflee, Maisarah, et al.
Publicado em: (2015)

Resampling algorithm for imbalanced data based on their neighbor relationship
por: Rui-feng LI, et al.
Publicado em: (2021-06-01)

Improving Sentiment Analysis Performance on Imbalanced Moroccan Dialect Datasets Using Resample and Feature Extraction Techniques
por: Zineb Nassr, et al.
Publicado em: (2025-01-01)

Validation assessments on resampling method in imbalanced binary classification for linear discriminant analysis
por: Jamaluddin, Ahmad Hakiim, et al.
Publicado em: (2021)

Validation assessments on Resampling Method in Imbalanced Binary Classification for Linear Discriminant Analysis
por: Jamaluddin, Ahmad Hakiim, et al.
Publicado em: (2021)

VALIDATION ASSESSMENTS ON RESAMPLING METHOD IN IMBALANCED BINARY CLASSIFICATION FOR LINEAR DISCRIMINANT ANALYSIS
por: Ahmad Hakiim Jamaluddin, et al.
Publicado em: (2020-11-01)

VALIDATION ASSESSMENTS ON RESAMPLING METHOD IN IMBALANCED BINARY CLASSIFICATION FOR LINEAR DISCRIMINANT ANALYSIS
por: Ahmad Jamaluddin, et al.
Publicado em: (2020-10-01)

Resample-Based Ensemble Framework for Drifting Imbalanced Data Streams
por: Hang Zhang, et al.
Publicado em: (2019-01-01)

Selecting the Suitable Resampling Strategy for Imbalanced Data Classification Regarding Dataset Properties. An Approach Based on Association Models
por: Mohamed S. Kraiem, et al.
Publicado em: (2021-09-01)

BALANCED VS IMBALANCED TRAINING DATA: CLASSIFYING RAPIDEYE DATA WITH SUPPORT VECTOR MACHINES
por: M. Ustuner, et al.
Publicado em: (2016-06-01)

ANALYZING THE IMPACT OF RESAMPLING METHOD FOR IMBALANCED DATA TEXT IN INDONESIAN SCIENTIFIC ARTICLES CATEGORIZATION
por: Ariani Indrawati, et al.
Publicado em: (2020-12-01)

CCR: A combined cleaning and resampling algorithm for imbalanced data classification
por: Koziarski Michał, et al.
Publicado em: (2017-12-01)

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 em: (2021-11-01)

A Novel Ensemble Framework Based on K-Means and Resampling for Imbalanced Data
por: Huajuan Duan, et al.
Publicado em: (2020-03-01)

Deep Learning-Based Imbalanced Classification With Fuzzy Support Vector Machine
por: Ke-Fan Wang, et al.
Publicado em: (2022-01-01)

An improved non−comparative classification method for human microRNA gene prediction
por: Batuwita, R, et al.
Publicado em: (2008)

Knowledge distillation with resampling for imbalanced data classification: Enhancing predictive performance and explainability stability
por: Kazuki Fujiwara
Publicado em: (2024-12-01)

Anomalous Propagation Echo Classification of Imbalanced Radar Data with Support Vector Machine
por: Hansoo Lee, et al.
Publicado em: (2016-01-01)

Increasing Minority Recall Support Vector Machine Model for Imbalanced Data Classification
por: Chunye Wu, et al.
Publicado em: (2021-01-01)

Machine Learning Model for Imbalanced Cholera Dataset in Tanzania
por: Judith Leo, et al.
Publicado em: (2019-01-01)

Robust Rescaled Hinge Loss Twin Support Vector Machine for Imbalanced Noisy Classification
por: Ling-Wei Huang, et al.
Publicado em: (2019-01-01)

Ensemble deep random vector functional link neural net for imbalanced datasets
por: Soo, Jian Xian
Publicado em: (2022)

Determining Resampling Ratios Using BSMOTE and SVM-SMOTE for Identifying Rare Attacks in Imbalanced Cybersecurity Data
por: Sikha S. Bagui, et al.
Publicado em: (2023-10-01)

Using Information on Class Interrelations to Improve Classification of Multiclass Imbalanced Data: A New Resampling Algorithm
por: Janicka Małgorzata, et al.
Publicado em: (2019-12-01)

Improved support vector machine classification for imbalanced medical datasets by novel hybrid sampling combining modified mega-trend-diffusion and bagging extreme learning machine model
por: Liang-Sian Lin, et al.
Publicado em: (2023-09-01)