Network Intrusion Detection Systems (NIDSs) play a vital role in detecting and stopping network attacks. However, the prevalent imbalance of training samples traffic interferes with NIDS detection performance. This paper proposes resampling method based on Self-Paced Ensemble Auxiliary Classifier Generative Adversarial Networks (SPE-ACGAN) to address problem sample classes. To deal class proble...

The imbalanced class distribution is one of the main issue in data mining. This problem exists in multi class imbalance, when samples containing in one class are greater or lower than that of other classes. Most existing imbalance learning techniques are only designed and tested for two-class scenarios. The new negative correlation learning (NCL) algorithm for classification ensembles, called A...

Fish-net algorithm is a novel field learning algorithm which derives classification rules by looking at the range of values of each attribute instead of the individual point values. In this paper, we present a Feature Selection Fish-net learning algorithm to solve the Dual Imbalance problem on text classification. Dual imbalance includes the instance imbalance and feature imbalance. The instanc...

In this paper we investigate the suitability of a fuzzy system as a classifier for imbalanced data problems. Primarily, the fuzzy model performance is evaluated on artificial data sets, generated with various levels of size, complexity and imbalance. It is investigated what combination of the three problematic issues makes the learning problem harder [4]. A theoretic analysis shows that for a f...