Classification in imbalanced domains is a recent challenge in data mining. We refer to imbalanced classification when data presents many examples from one class and few from the other class, and the less representative class is the one which has more interest from the point of view of the learning task. One of the most used techniques to tackle this problem consists in preprocessing the data pr...

Abstract—Although learning on non-stationary data and imbalanced data have been extensively studied in the literature separately, however little work has been done to tackle the imbalanced issue on nonstationary data stream as the joint probability distribution between the data and classes changes with time and may results skewed class distribution. Especially in airlines delay detection, data ...

Arrhythmias are irregularities in the heartbeat and can be life-threatening. Early diagnosis of Cardiac Arrhythmia is quite crucial for saving patient lives. In this study, main goal to detect presence cardiac arrhythmia classify it into 16 groups from ECG recordings. The dataset UCI databank used apply different network structures classification. number sample each class not same dataset. has ...

Classification is one of the most important research contents in data mining and traditional classification methods are relatively mature, when dealing with well-balanced data they can make good performances. But in real world the data is usually imbalanced, that is, most of the data are in majority class and little data are in minority class. Imbalanced data set cause the deduction of the prec...

The random sets approach is heuristic in nature and has been inspired by the growing speed of computations. For example, we can consider a large number of classifiers where any single classifier is based on a relatively small subset of randomly selected features or random sets of features. Using cross-validation we can rank all random sets according to the selected criterion, and use this ranki...

Imbalanced data sets have significantly unequal distributions between classes. This between-class imbalance causes conventional classification methods to favor majority classes, resulting in very low or even no detection of minority classes. A Min-Max modular support vector machine (M-SVM) approaches this problem by decomposing the training input sets of the majority classes into subsets of sim...

In many real application areas, the data used are highly skewed and 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 paper is to improve the performance of fuzzy rule based classification systems on imb...

In recent years, high-throughput technologies such as DNA sequencing and microarrays have created the need for automated annotation and analysis of large sets of genes. The Gene Ontology (GO) provides a common controlled vocabulary for describing gene function however the process for annotating proteins with GO terms is usually through a tedious manual curation process by trained profession ann...

Various modifications of bagging for class imbalanced data are discussed. An experimental comparison of known bagging modifications shows that integrating with undersampling is more powerful than oversampling. We introduce Local-and-Over-All Balanced bagging where probability of sampling an example is tuned according to the class distribution inside its neighbourhood. Experiments indicate that ...

Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with small amount of data. We develop a latent variable model that can cope with imbalanced data by dividing the latent space into a shared space and a private space. Based on Gaussian Process Latent Variable Models, we pr...