The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

Learning and mining from imbalanced datasets gained increased interest in recent years.
One simple but efficient way to increase the performance of standard machine learning …

This study investigates how to alleviate the class imbalance problems for constructing
unbiased classifiers when instances in one class are more than that in another. Since …

Several oversampling methods have been proposed for solving the class imbalance
problem. However, most of them require searching the k-nearest neighbors to generate …

Universitas Pamulang berusaha memberikan pendidikan tinggi dengan biaya yang
terjangkau oleh kalangan bawah. Tetapi mahasiswanya banyak yang keluar di tiap …

Quality variables are key indicators of the operating performance in industrial processes.
Because they are difficult to measure, soft sensor models can be adopted to predict them …

Dataset software metrics secara umum bersifat tidak seimbang, hal ini dapat menurunkan
kinerja model prediksi cacat software karena cenderung menghasilkan prediksi kelas …

With the rapid development of the Internet of Things (IoT), the frequency of attackers using
botnets to control IoT devices in order to perform distributed denial-of-service attacks (DDoS) …

Handling an imbalanced class problem is a challenging task in real-world applications. This
problem affects various prediction models that predict only the majority class and fail to …

It is difficult for learning models to achieve high classification performances with imbalanced
data sets, because with imbalanced data sets, when one of the classes is much larger than …