Results 1 to 10 of about 13,846 (260)
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.
Mohammed Badawy
exaly +4 more sources
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 +5 more sources
Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark
Neurocomputing, 2021 “La Caixa” Foundation, under agreement LCF/PR/PR18/51130007. This work was supported by the Junta de Castilla y León under project BU055P20 and by the Ministry of Science and Innovation of Spain under project PID2020-119894 GB-I00, co-financed through European Union FEDER funds.
Alvar Arnaiz-González +2 more
exaly +4 more sources
Geometric SMOTE a geometrically enhanced drop-in replacement for SMOTE
Information Sciences, 2019 Abstract Classification of imbalanced datasets is a challenging task for standard algorithms. Although many methods exist to address this problem in different ways, generating artificial data for the minority class is a more general approach compared to algorithmic modifications.
Georgios Douzas, Fernando Bacao
exaly +3 more sources
Journal of Computer and Creative Technologyปัญหาความไม่สมดุลของข้อมูลในกระบวนการเรียนรู้ของเครื่องเป็นข้อจำกัดสำคัญที่ส่งผลต่อประสิทธิภาพของโมเดล โดยเฉพาะในกรณีที่กลุ่มข้อมูลกลุ่มน้อยมีจำนวนน้อยกว่ากลุ่มข้อมูลกลุ่มใหญ่ ทำให้โมเดลเรียนรู้มีความลำเอียงและจำแนกข้อมูลได้ไม่แม่นยำ วิธีการแก้ไขปัญหานี้
วริสรา วสุอารยะศักดิ์ +3 more
doaj +2 more sources
CoRR, 2022 Balancing the data before training a classifier is a popular technique to address the challenges of imbalanced binary classification in tabular data. Balancing is commonly achieved by duplication of minority samples or by generation of synthetic minority samples.
Yotam Elor, Hadar Averbuch-Elor
openaire +2 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
doaj +1 more source
AGNES-SMOTE: An Oversampling Algorithm Based on Hierarchical Clustering and Improved SMOTE [PDF]
Scientific Programming, 2020 Aiming at low classification accuracy of imbalanced datasets, an oversampling algorithm—AGNES-SMOTE (Agglomerative Nesting-Synthetic Minority Oversampling Technique) based on hierarchical clustering and improved SMOTE—is proposed. Its key procedures include hierarchically cluster majority samples and minority samples, respectively; divide minority ...
Xin Wang +6 more
openaire +1 more source
Prediction of the Road Accidents Severity Level: Case of Saint-Petersburg and Leningrad Oblast
International Journal of Technology, 2023 This article examines the factors influencing the severity of road accidents in St. Petersburg and Leningrad oblast for 2015–2023. The study is carried out on the analysis of 69190 road accidents and 6 groups of factors using the logit model and ...
Angi Skhvediani +3 more
doaj +1 more source
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 +1 more source