Results 11 to 20 of about 18,911 (296)
SMOTE-CD: SMOTE for compositional data.
PLoS ONE, 2023 Compositional data are a special kind of data, represented as a proportion carrying relative information. Although this type of data is widely spread, no solution exists to deal with the cases where the classes are not well balanced.
Teo Nguyen +3 more
doaj +6 more sources
Two Novel SMOTE Methods for Solving Imbalanced Classification Problems
IEEE Access, 2023 The imbalanced classification problem has always been one of the important challenges in neural network and machine learning. As an effective method to deal with imbalanced classification problems, the synthetic minority oversampling technique (SMOTE ...
Yuan Bao, Sibo Yang
exaly +4 more sources
RN-SMOTE: Reduced Noise SMOTE based on DBSCAN for enhancing imbalanced data classification
Journal of King Saud University - Computer and Information Sciences, 2022 Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot of the real-world data sets are naturally imbalanced. So, imbalanced classification is a serious problem in machine learning.
Nawal El-Fishawy, Mohammed Badawy
exaly +4 more sources
, 2023 FLEX-SMOTE: Synthetic Over-sampling TEchnique that Flexibly Adjusts to Different Minority Class Distributions
CHUMPHOL BUNKHUMPORNPAT (16496058)
openaire +2 more sources
DTO-SMOTE: Delaunay Tessellation Oversampling for Imbalanced Data Sets
Information, 2020 One of the significant challenges in machine learning is the classification of imbalanced data. In many situations, standard classifiers cannot learn how to distinguish minority class examples from the others.
Alexandre M. de Carvalho +1 more
doaj +2 more sources
IEEE AccessAnemia is a widespread worldwide health problem that has a substantial effect on groups who are particularly susceptible. The objective of this work is to improve the diagnosis of anemia by creating a hybrid machine learning model called SMOTE-MRS.
Dimas Chaerul Ekty Saputra +2 more
doaj +2 more sources
SMOTE-OB: Combining SMOTE and Online Bagging for Continuous Rebalancing of Evolving Data Streams
2021 IEEE International Conference on Big Data (Big Data), 2021 The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift.
Bernardo, Alessio, Valle, Emanuele Della
openaire +3 more sources
Journal of Computer and Creative Technologyปัญหาความไม่สมดุลของข้อมูลในกระบวนการเรียนรู้ของเครื่องเป็นข้อจำกัดสำคัญที่ส่งผลต่อประสิทธิภาพของโมเดล โดยเฉพาะในกรณีที่กลุ่มข้อมูลกลุ่มน้อยมีจำนวนน้อยกว่ากลุ่มข้อมูลกลุ่มใหญ่ ทำให้โมเดลเรียนรู้มีความลำเอียงและจำแนกข้อมูลได้ไม่แม่นยำ วิธีการแก้ไขปัญหานี้
วริสรา วสุอารยะศักดิ์ +3 more
doaj +2 more sources
SMOTE-D, Una versión determinista de SMOTE [PDF]
, 2017 En diferentes aplicaciones prácticas es común que se presente desbalance entre clases. Este problema aparece cuando la cantidad de objetos en una clase es mucho menor que en la otra. Esta diferencia en el tamaño de las clases causa que los métodos de clasificación favorezcan a la clase con mayor cantidad de objetos (mayoritaria), produciendo un mal ...
FREDY RODRIGUEZ TORRES
openaire +2 more sources
IEEE AccessClass-imbalance problems have become a key challenge in machine learning, often results in training too many majority samples and learning too few minority samples.
Yongjie Huang
doaj +2 more sources