Results 1 to 10 of about 12,774 (258)
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.
Ahmed Arafa +3 more
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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 One of the main goals of Big Data research, is to find new data mining methods that are able to process large amounts of data in acceptable times. In Big Data classification, as in traditional classification, class imbalance is a common problem that must be addressed, in the case of Big Data also looking for a solution that can be applied in an ...
Mario Juez-Gil +2 more
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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 +3 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
SMOTE-ENC: A Novel SMOTE-Based Method to Generate Synthetic Data for Nominal and Continuous Features [PDF]
Applied System Innovation, 2021 Real-world datasets are heavily skewed where some classes are significantly outnumbered by the other classes. In these situations, machine learning algorithms fail to achieve substantial efficacy while predicting these underrepresented instances.
Matloob Khushi
exaly +4 more sources
Journal of Computer and Creative Technologyปัญหาความไม่สมดุลของข้อมูลในกระบวนการเรียนรู้ของเครื่องเป็นข้อจำกัดสำคัญที่ส่งผลต่อประสิทธิภาพของโมเดล โดยเฉพาะในกรณีที่กลุ่มข้อมูลกลุ่มน้อยมีจำนวนน้อยกว่ากลุ่มข้อมูลกลุ่มใหญ่ ทำให้โมเดลเรียนรู้มีความลำเอียงและจำแนกข้อมูลได้ไม่แม่นยำ วิธีการแก้ไขปัญหานี้
วริสรา วสุอารยะศักดิ์ +3 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
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
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