Importance-SMOTE: a synthetic minority oversampling method for noisy imbalanced data
J Liu - Soft Computing, 2022 - Springer
Synthetic minority oversampling methods have been proven to be an efficient solution for
tackling imbalanced data classification issues. Different strategies have been proposed for …
tackling imbalanced data classification issues. Different strategies have been proposed for …
[HTML][HTML] A three-way decision ensemble method for imbalanced data oversampling
Abstract Synthetic Minority Over-sampling Technique (SMOTE) is an effective method for
imbalanced data classification. Many variants of SMOTE have been proposed in the past …
imbalanced data classification. Many variants of SMOTE have been proposed in the past …
Undersampling method based on minority class density for imbalanced data
Z Sun, W Ying, W Zhang, S Gong - Expert Systems with Applications, 2024 - Elsevier
Imbalanced data severely hinder the classification performance of learning-based
algorithms and attract a great deal of attention from researchers. The undersampling method …
algorithms and attract a great deal of attention from researchers. The undersampling method …
Preprocessing method based on sample resampling for imbalanced data of electronic circuits.
LI Ruifeng, XU Aiqiang, SUN Weichao… - Systems …, 2020 - search.ebscohost.com
In order to solve the deficiency of fault state data and imbalance of whole test data in
airborne electronic circuit, a data preprocessing method based on sample resampling is …
airborne electronic circuit, a data preprocessing method based on sample resampling is …
A novel distribution analysis for smote oversampling method in handling class imbalance
Class Imbalance problems are often encountered in many applications. Such problems
occur whenever a class is under-represented, has a few data points, compared to other …
occur whenever a class is under-represented, has a few data points, compared to other …
A cluster-based oversampling algorithm combining SMOTE and k-means for imbalanced medical data
The algorithm of C4. 5 decision tree has the advantages of high classification accuracy, fast
calculation speed and comprehensible classification rules, so it is widely used for medical …
calculation speed and comprehensible classification rules, so it is widely used for medical …
[PDF][PDF] 不平衡数据中基于异类k 距离的边界混合采样
于艳丽， 江开忠， 盛静文 - 计算机应用与软件, 2021 - shcas.net
摘要不平衡数据集中, 样本的分布位置对于决策边界具有差异性, 传统的采样方法没有根据样本
位置做区别化采样处理. 为此提出一种不平衡数据中基于异类k 距离的边界混合采样算法(BHSK) …
位置做区别化采样处理. 为此提出一种不平衡数据中基于异类k 距离的边界混合采样算法(BHSK) …
面向非平衡多分类问题的二次合成QSMOTE 方法
韩明鸣， 郭虎升， 王文剑 - 南京大学学报(自然科学版), 2019 - jns.nju.edu.cn
近年来非平衡多分类数据的学习问题在机器学习和数据挖掘领域备受关注, 上采样技术成为解决
数据不平衡问题的主要方法, 然而已有的上采样技术仍有很多的不足, 例如新合成的少数类样本 …
数据不平衡问题的主要方法, 然而已有的上采样技术仍有很多的不足, 例如新合成的少数类样本 …
基于改进SMOTE 的不平衡数据挖掘方法研究
杨智明， 乔立岩， 彭喜元 - 电子学报, 2007 - cqvip.com
少类样本合成过采样技术(SMOTE) 是一种新型的过采样方法, 能够有效地处理不平衡数据分类
问题, 但SMOTE 在产生合成样本的过程中, 存在一定的盲目性. 因此本文提出一种改进的过采样 …
问题, 但SMOTE 在产生合成样本的过程中, 存在一定的盲目性. 因此本文提出一种改进的过采样 …