The proposed method, called SMOTE Density Based Support Vector Machine (SDB-SVM) considers unbalanced data sets, we are going to present a way of handling unbalanced data sets by resampling methods (undersampling and oversampling). We will implement DB-SVM to our data set to clean it, then ...
面向不平衡数据集的改进型SMOTE算法 针对SMOTE(synthetic minority over-sampling technique)在合成少数类新样本时存在的不足,提出了一种改进的SMOTE算法GA-SMOTE.该算法的关键将是遗传算法中的3个基本算... 王超学,张涛,马春森 - 《计算机科学与探索》 被引量: 12发表: 2014年 Class Switching according to Nearest...
Knowl. Based Syst. (2016) G. Douzas et al. Improving imbalanced learning through a heuristic oversampling method based on k-means and smote Inf. Sci. (2018) J. Hou et al. Towards parameter-independent data clustering and image segmentation Pattern Recognit. (2016) A. Jain Data clustering:...
Kernel-based SMOTE for SVM classification of imbalanced datasets Datasets with an imbalanced class distribution pose a severe challenge to traditional learning algorithms that are designed to improve overall classificati... J Mathew,L Ming,CK Pang,... - Conference of the IEEE Industrial Electronics ...
An investigation of SMOTE based methods for imbalanced datasets with data complexity analysis 2023, IEEE Transactions on Knowledge and Data Engineering A Chaotic-Based Interactive Autodidactic School Algorithm for Data Clustering Problems and Its Application on COVID-19 Disease Detection 2023, Symmetry Eff...
To handle the imbalanced classification problems, many useful approaches have been developed, for example, synthetic minority oversampling technique (SMOTE). However, the SMOTE is often sensitive to the predetermined k k value, i.e., the number of nearest neighbors used to generate the synthetic ...
SMOTE has been favored by researchers in improving imbalanced classification. Nevertheless, imbalances within minority classes and noise generation are two main challenges in SMOTE. Recently, clustering-based oversampling methods are developed to improve SMOTE by eliminating imbalances within minority classes...
Finally, sampling weights are assigned to each cluster, and the minority class of samples suitable for oversampling is selected for synthetic minority oversampling (SMOTE) by calculating the local sparsity of the samples. Experiments on 18 imbalanced data sets show that ND-S is effective for the...
In this paper, a novel oversampling method based on local density estimation, namely LD-SMOTE, is presented to address constraints of the popular rebalance technique SMOTE. LD-SMOTE initiates with k-means clustering to quantificationally measure the classification contribution of each feature. ...
In this paper, a novel oversampling method based on local density estimation, namely LD-SMOTE, is presented to address constraints of the popular rebalance technique SMOTE. LD-SMOTE initiates with k-means clustering to quantificationally measure the classification contribution of each feature. ...