Then, to solve the problems of traditional cluster-based oversampling, we propose a k-means cluster-based filtering strategy. Define a matrix of original sample classes, perform class difference calculations on the clustered samples, and screen out ‘‘safe samples” that have no change in sample...
Standard oversampling methods can be used to improve the dataset class distribution; however, they do not consider the ordinal relationship between the classes. The proposed CWOS-Ord method aims to address this problem by first clustering minority classes and then oversampling them based on their ...
Synthetic over-sampling is a well-known method to solve class imbalance by modifying class distribution and generating synthetic samples. A large number of synthetic over-sampling techniques have been proposed; however, most of them suffer from the over-generalization problem whereby synthetic minority ...
Hence, an under-sampling ap- proach is to decrease the skewed distribution of MA and MI by lowering the size of MA. Generally, the performances of over-sam- pling approaches are worse than that of under-sampling ap- proaches (Drummond & Holte, 2003). One simple method of under-sampling...
Li J, Fong S, and Sung Y, "Adaptive swarm cluster- based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification," Biodata Mining, 2016, 9(1):37.
This paper proposes a novel over-sampling strategy to handle imbalanced data based on cluster ensembles, named CE-SMOTE, which aims to provide a better training platform by introducing clustering consistency index to find out the clus...
However, most of the oversampling methods may introduce noise and fuzzy boundarie... Y Zhang,L Deng,B Wei - Mathematics (2227-7390) 被引量: 0发表: 2024年 A clustering-optimized segmentation algorithm and application on food quality detection segmented by a distance function between categories ...
In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and...
Ashhadul Islam, Belhaouari Samir Brahim, Rehman Atiq Ur, Bensmail Halima (2022) KNNOR: an oversampling technique for imbalanced datasets. Appl Soft Comput 115:1–15 Google Scholar Hongle Du, Zhang Yan, Gang Ke, Zhang Lin, Chen Yeh-Cheng (2021) Online ensemble learning algorithm for imbal...
The class-based (ABC) storage policy considers two product attributes to assign products to storage locations, namely turnover speed and storage space needed. The former refers to how often a product is ordered in a certain period. The latter is the average amount of space that is required ...