We assessed the efficacy of oversampling techniques to enhance machinelearning model performance in predicting Escherichia coli MG1655 presencein spinach wash water. Three oversampling methods were applied to balancetwo datasets, forming the basis for training random forest (RF), support vectormachines ...
The classification of imbalanced datasets is a prominent task in text mining and machine learning. The number of samples in each class is not uniformly distributed; one class contains a large number of samples while the other has a small number. Overfitt
Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor Oversampling-Regression (KNNOR-Reg), originally for imbalanced classification, to address imbalanced regression in low populat...
Imbalanced learning is an important branch of machine learning. It addresses the challenge of improving classifier accuracy for minority classes in imbalan... X Lu,X Ye,Y Cheng - 《Engineering Applications of Artificial Intelligence》 被引量: 0发表: 2024年 Radial-Based Oversampling for Multiclass...
Missing labels in multi-label datasets are a common problem, especially for minority classes, which are more likely to occur. This limitation hinders the p
We comprehensively evaluated the proposed approach against four advanced machine learning and two deep learning methods. In addition, we used a hyperparameter optimization approach to enhance the performance of the proposed approach for the diagnosis of thyroid disease. ...
Imbalanced learning is a basic problem in machine learning. When the number of samples from different categories in a classification task dataset differs significantly, the dataset is called imbalanced. Typically, the category with the larger number of samples is called the majority category, and that...
In practical applications of machine learning, the class distribution of the collected training set is usually imbalanced, i.e., there is a large differenc... Q Zhou,B Sun - 《International Journal of Applied Mathematics & Computer Science》 被引量: 0发表: 2024年 OALDPC: oversampling approach...
Keywords: transverse dispersion coefficient; imbalanced dataset; data oversampling; machine learning; nonlinear regression 1. Introduction Water quality management is a significant task for public health and aquatic environ- ments. The mixing stages of introduced polluted water in natural rivers are ...
Imbalanced datasets are those where there is a severe skew in the class distribution, such as 1:100 or 1:1000 examples in the minority class to the majority class. This bias in the training dataset can influence many machine learning algorithms, leading some to ignore the minority class entire...