In this paper, we introduce weights into Pawlak rough set model to balance the class distribution of a data set and develop a weighted rough set based method to deal with the class imbalance problem. In order to develop the weighted rough set based method, we design first a weighted attribute reduction algorithm by introducing and extending Guiasu weighted entropy to measure the significance of...

Integration of deep learning into Intrusion Detection Systems (IDS) for Software Defined Networking (SDN) is an emerging field research. Most the datasets used to build IDS are highly imbalanced, especially in case DDoS attacks, which account a larger percentage malicious samples than normal traffic. As result class imbalance, classification distorted since limited its ability generalize and mi...

Multi-label data classification has become an important and active research topic, where the classification algorithm is required to deal with prediction of sets of label indicators for instances simultaneously. Label powerset (LP) method reduces the multi-label classification problem to a single-label multi-class classification problem by treating each distinct combination of labels. However, ...

We show that the novelty detection approach is a viable solution to the class imbalance and examine which approach is suitable for different degrees of imbalance. In experiments using SVM-based classifiers, when the imbalance is extreme, novelty detectors are more accurate than balanced and unbalanced binary classifiers. However, with a relatively moderate imbalance, balanced binary classifiers...

In recent years, imbalanced learning problem has attracted more and more attentions from both academia and industry, and the problem is concerned with the performance of learning algorithms in the presence of data with severe class distribution skews. In this paper, we apply the well-known statistical model logistic discrimination to this problem and propose a novel method to improve its perfor...

Class imbalance and overlapping on multi-class can reduce the performance accuracy of classification. Noise must also be considered because it With a resampling algorithm feature selection, this paper proposes method for improving hybrid approach redefinition-multi class (HAR-MI). Resampling overcome problem noise but cannot handle well. Feature selection is good at dealing with experience decr...