-The class imbalanced problem occurs in various disciplines when one of target classes has a small number of instances compare to other classes. A classifier normally ignores or neglects to detect a minority class due to the small number of class instances. It poses a challenge to any classifier as it becomes hard to learn the minority class samples. Most of the oversampling methods may generat...

In classification, when the distribution of the training data among classes is uneven, the learning algorithm is generally dominated by the feature of the majority classes. The features in the minority classes are normally difficult to be fully recognized. In this paper, a method is proposed to enhance the classification accuracy for the minority classes. The proposed method combines Synthetic ...

BACKGROUND The IOM report, Preventing Medication Errors, emphasizes the overall lack of knowledge of the incidence of adverse drug events (ADE). Operating rooms, emergency departments and intensive care units are known to have a higher incidence of ADE. Labor and delivery (L&D) is an emergency care unit that could have an increased risk of ADE, where reported rates remain low and under-reportin...

Imbalanced data is a challenge for classification models. It reduces the overall performance of traditional learning algorithms. Besides, minority class imbalanced datasets misclassified with high ratio even though this crucial object process. In paper, new model called Lasso-Logistic ensemble proposed to deal by utilizing two popular techniques, random over-sampling and under-sampling. The was...

Many real-life datasets suffer from class imbalance, where one or more classes are under-represented in the dataset, resulting reduced classifier performance, with expected decline quality of procedures depending on classification results, such as financial losses to businesses inferior product quality. Improving accuracy by handling imbalance will positively impact accuracy. In this study, we ...