TY  - JOUR
A1  - Jabir, Brahim
A1  - Díez, Isabel De la Torre
A1  - Bautista Thompson, Ernesto
A1  - Ramírez-Vargas, Debora L.
A1  - Kuc Castilla, Ángel Gabriel
AV  - none
KW  - Ensemble Partition Sampling (EPS); One vs One (OvO); One vs All (OvA); Multi-Class Classification; Imbalanced learning; multiclass imbalanced classification
TI  - Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification
EP  - 1
ID  - uneatlantico7028
UR  - http://doi.org/10.1109/ACCESS.2023.3273925
SN  - 2169-3536
Y1  - 2023///
JF  - IEEE Access
N2  - Classification is a commonly used technique in data mining and is applied in various fields such as sentiment analysis, fraud detection, and fault diagnosis. Multiclass classification, which involves more than two classes, is more complex than binary classification. There are mainly two ways to approach multiclass classification, one is to expand the binary classifier into a multiclass classifier through various strategies and the other is to divide the multiclass classification problem into multiple binary problems (binarization). Two popular approaches for binarization are One vs One (OvO) and One vs All (OvA). It is simpler to aggregate the outputs of all binary classifiers as the number of classifiers decreases. However, it causes an imbalance of positive and negative sample numbers, which affects the classification effect of each binary classifier. In this article, we contribute to the field of ensemble learning and multi-class classification by proposing a new method called Ensemble Partition Sampling (EPS). This article presents a new approach to multiclass classification using an "Ensemble Partition Sampling" method within the "one-vs-all" (OvA) framework. The primary goal of this method is to tackle the problem of data imbalance by incorporating ensemble learning and preprocessing techniques into each binary dataset. The study found that Ensemble Partition Sampling (EPS) is the most effective method for imbalanced and multiclass imbalanced classification, outperforming other methods including OvA, SMOTE, k-means-SMOTE, Bagging-RB, DES-MI, OvO-EASY, and OvO-SMB. The study used CART, Random Forest, and SVM as classifiers, and the results consistently showed that EPS outperformed all other algorithms. The findings suggest that EPS is a highly effective method for improving classification performance in imbalanced and multiclass imbalanced datasets.
SP  - 1
ER  -