In computer networks, Network Intrusion Detection System (NIDS) plays a very important role in identifying intrusion behaviors. NIDS can identify abnormal behaviors by analyzing network traffic. However, the performance of classifier is not good traffic for minority classes. order to improve detection rate on class imbalanced dataset, we propose model based two-layer CNN and Cluster-SMOTE + K-m...

To improve the classification performance of imbalanced learning, a novel over-sampling method, Global Immune Centroids OverSampling (Global-IC) based on an immune network, is proposed. GlobalIC generates a set of representative immune centroids to broaden the decision regions of small class spaces. The representative immune centroids are regarded as synthetic examples in order to resolve the i...

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...

This contribution proposes a powerful technique for two-class imbalanced classification problems by combining the synthetic minority over-sampling technique (SMOTE) and the particle swarm optimisation (PSO) aided radial basis function (RBF) classifier. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is appli...

Clinicians are required to make an early prediction of diseases save a life, especially cerebrovascular diseases. The objective this research is use mathematical models such as boosting machine learning algorithms tool be applied by clinicians for disease. This paper particularly, considered XGBoost, AdaBoost, LightGBM, and CatBoost Classifiers predict disease using age, gender, BMI, hypertensi...

In this paper, we present a prototype selection technique for imbalanced data, Fuzzy Rough Imbalanced Prototype Selection (FRIPS), to improve the quality of the artificial instances generated by the Synthetic Minority Over-sampling TEchnique (SMOTE). Using fuzzy rough set theory, the noise level of each instance is measured, and instances for which the noise level exceeds a certain threshold le...

Abstract Class imbalance occurs when the class distribution is not equal. Namely, one under-represented (minority class), and other has significantly more samples in data (majority class). The problem prevalent many real world applications. Generally, minority of interest. synthetic over-sampling technique (SMOTE) method considered most prominent for handling unbalanced data. SMOTE generates ne...

Learning from imbalanced data is a problem which arises in many real-world scenarios, so does the need to build classifiers able to predict more than one class label simultaneously (multilabel classification). Dealing with imbalance by means of resampling methods is an approach that has been deeply studied lately, primarily in the context of traditional (non-multilabel) classification. In this ...

In many real classification problems the data are imbalanced, i.e., the number of instances for some classes are much higher than that of the other classes. Solving a classification task using such an imbalanced data-set is difficult due to the bias of the training towards the majority classes. The aim of this contribution is to analyse the performance of CORBFN, a cooperative-competitive evolu...

Accurate diagnosis of cancer is of great importance due to the global increase in new cancer cases. Cancer researches show that diagnosis by using microarray gene expression data is more effective compared to the traditional methods. This study presents an extensive evaluation of a variant of Deep Belief Networks Discriminative Deep Belief Networks (DDBN) in cancer data analysis. This new neura...