Abstract Machine learning plays an increasingly significant role in the building of Network
Intrusion Detection Systems. However, machine learning models trained with imbalanced …

Real-world applications often involve imbalanced datasets, which have different
distributions of examples across various classes. When building a system that requires a …

Presently, security is a hot research topic due to the impact in daily information infrastructure.
Machine-learning solutions have been improving classical detection practices, but detection …

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

One of the main goals of Big Data research, is to find new data mining methods that are able
to process large amounts of data in acceptable times. In Big Data classification, as in …

The training machine learning algorithm from an imbalanced data set is an inherently
challenging task. It becomes more demanding with limited samples but with a massive …

In the production process of hot continuous rolling, due to the imbalance between the
number of normal cases and fault cases, the traditional supervised learning methods are …

Datasets are growing in size and complexity at a pace never seen before, forming ever
larger datasets known as Big Data. A common problem for classification, especially in Big …

Dengesiz veri sınıflandırması, sınıflandırıcıların daha büyük veri sınıfına doğru çarpıtıldığı
veri madenciliğinde yaygın bir konudur. Yüksek boyutlu çarpık (dengesiz) verilerin …

Addressing the huge amount of data continuously generated is an important challenge in
the Machine Learning field. The need to adapt the traditional techniques or create new ones …