Results 231 to 240 of about 13,846 (260)

Deterministic oversampling methods based on SMOTE

Journal of Intelligent & Fuzzy Systems, 2019
In supervised classification if one of the classes has fewer objects than the other, we have a class imbalance problem. One of the most common solutions to address class imbalance problems is oversampling, and SMOTE is the most referenced and well-known oversampling method. However, SMOTE creates synthetic objects in a random way, therefore it produces
Fredy Rodríguez Torres, Jesús Ariel Carrasco-Ochoa, José Fco. Martínez-Trinidad   +2 more
openaire   +1 more source

Geometric SMOTE for regression

Expert Systems with Applications, 2022
Luís Camacho, Georgios Douzas, Fernando Bação   +2 more
openaire   +1 more source

The Application of SMOTE Algorithm for Unbalanced Data

Proceedings of the 2018 International Conference on Artificial Intelligence and Virtual Reality, 2018
The current power user data is unbalanced when it is used to analyze the behavior of the leakage user. In other words, the normal user data and the leakage user data have an inconsistent scale. When the automatic identification model of the leakage user is established, the analysis of the information of the leakage user's behavior feature is not clear,
Dong Lv, ZhiCheng Ma, Shibo Yang, Xianbo Li, Zhixin Ma 0002, Fan Jiang   +5 more
openaire   +1 more source

XGBoost for Educational Performance: Comparing SMOTE and SMOTE-TOMEK on Imbalanced Data

Proceeding International Collaborative Conference on Multidisciplinary Science
Class imbalance poses a critical challenge in educational performance prediction, particularly in accurately identifying at-risk students within small datasets. This study rigorously evaluates three data balancing strategies—baseline imbalanced processing, SMOTE (Synthetic Minority Over-sampling Technique), and SMOTE-TOMEK—integrated with the XGBoost ...
null Ucta Pradema Sanjaya, null Purwosiwi Pandansari, null Ade Pratama, null Rina Purwanti, null Noor Laila Ramadhani, null Ari Eko Budiyanto, null Kristanti Kristanti, null Iwan Setiawan Wibisono, null Kustiyono Kustiyono, null Agung Wibowo, null Sri Mujiyono   +10 more
openaire   +1 more source

Automatic Determination of Neighborhood Size in SMOTE

Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication, 2016
In order to handle the class imbalance problem, synthetic data generation methods such as SMOTE, ADASYN, and Borderline-SMOTE have been developed. These methods use a common parameter k, the number of nearest neighbors. Nonetheless the most effective k value depends on the given dataset, there is no guideline to determine k.
Jaesub Yun, Jihyun Ha, Jong-Seok Lee
openaire   +1 more source

SMOTE vs. ADASYN

Significant technical progress has led to an expansion in human requirements. As a result, the banking sector has seen a rise in the quantity of loan approval requests. When choosing a candidate for loan approval, a number of factors are taken into account to determine the loan's status.
Sabyasachi Pramanik, Soma Bag, Atanu Roy, Ramkrishna Ghosh, Pranati Rakshit   +4 more
openaire   +1 more source

SMOTE-OB: Combining SMOTE and Online Bagging for Continuous Rebalancing of Evolving Data Streams

2021 IEEE International Conference on Big Data (Big Data), 2021
Bernardo, Alessio, Valle, Emanuele Della
openaire   +2 more sources

SMOTE-RkNN: A hybrid re-sampling method based on SMOTE and reverse k-nearest neighbors

Information Sciences, 2022
Hualong Yu, Shang Gao, Xibei Yang
exaly  

A Comparison of Undersampling, Oversampling, and SMOTE Methods for Dealing with Imbalanced Classification in Educational Data Mining

Information (Switzerland), 2023
Okan Bulut, Tarid Wongvorachan, Surina He   +2 more
exaly  

Investigation on the stability of SMOTE-based oversampling techniques in software defect prediction

Information and Software Technology, 2021
Shuo Feng, Yan Xiao, Jacky Keung
exaly