Learning from imbalanced data is an important problem in data mining research. Much research has addressed the problem of imbalanced data by using sampling methods to generate an equally balanced training set to improve the performance of the prediction models, but it is unclear what ratio of class distribution is best for training a prediction model. Bagging is one of the most popular and effe...

Associative Classification (AC) is a well known tool in knowledge discovery and it has been proved to extract competitive classifiers. However, imbalanced data has posed a challenge for most classifier learn ing algorithms including AC methods. Because in the AC process, Interestingness Measure (IM) p lays an important role to generate interesting rules and build good classifiers, it is very im...

• Proposal of potential resemblance loss for measuring relative class distribution shape. unified over and undersampling framework based on resemblance. data difficulty index evaluation dataset complexity. Experimental the proposed approach. Examination factors influencing performance Data imbalance remains one negatively affecting contemporary machine learning algorithms. One most common appro...

Fuzzy rule-based classification system (FRBCS) is a popular machine learning technique for classification purposes. One of the major issues when applying it on imbalanced data sets is its biased to the majority class, such that, it performs poorly in respect to the minority class. However many cases the minority classes are more important than the majority ones. In this paper, we have extended ...

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...

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...