Several oversampling methods have been proposed for solving the class imbalance problem. However, most of them require searching k-nearest neighbors to generate synthetic objects. This requirement makes time-consuming and therefore unsuitable large datasets. In this paper, an method problems that do not neighbors’ search is proposed. According our experiments on datasets with different sizes im...

This paper introduces a framework that allows to mitigate the impact of class imbalance on most scalar performance measures when used to evaluate the behavior of classifiers. Formally, a correction function is defined with the aim of highlighting those classification results that present moderately higher prediction rates on the minority class. Besides, this function punishes those scenarios th...

With an advance in technologies, different tumor features have been collected for Breast Cancer (BC) diagnosis, processing of dealing with large data set suffers some challenges which include high storage capacity and time require for accessing and processing. The objective of this paper is to classify BC based on the extracted tumor features. To extract useful information and diagnose the tumo...

Image classification research is one of the fields continuously studied in computer vision domain, and several related studies have been actively conducted until recently. However, a limit exists regarding prediction performance real-world datasets due to data imbalance problem between classes. Data augmentation through artificial sample generation for minority classes methods used overcome thi...

Credit scoring is often modeled as a binary classification task where defaults rarely occur and the classes generally are highly unbalanced. Although many new algorithms have been proposed in the recent past to mitigate this specific problem, the aspect of class imbalance is still underrepresented in research despite its great relevance for many business applications. Within the “Machine Learni...

Learning from unbalanced datasets presents a convoluted problem in which traditional learning algorithms typically perform poorly. The heuristics used in learning tend to favor the larger, less important classes in such problems. While other methods, like sampling, have been introduced to combat imbalance, these tend to be computationally expensive. This paper proposes Hellinger distance as a m...

Abstract There is a class-imbalance problem that the number of minority class samples significantly lower than majority in common network traffic datasets. Class-imbalance phenomenon will affect performance classifier and reduce robustness to detect unknown anomaly detection. And distribution continuous features dataset does not follow Gaussian distribution, which bring great difficulties intru...

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...

In Data Mining the class Imbalance classification problem is considered to be one of the emergent challenges. This problem occurs when the number of examples that represents one of the classes of the dataset is much lower than the other classes. To tackle with imbalance problem, preprocessing the datasets applied with oversampling method (SMOTE) was previously proposed. Generalized instances ar...

The problem of detecting a small number of outliers in a large dataset is an important task in many fields from fraud detection to high-energy physics. Two approaches have emerged to tackle this problem: unsupervised and supervised. Supervised approaches require a sufficient amount of labeled data and are challenged by novel types of outliers and inherent class imbalance, whereas unsupervised m...