• 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...

Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information estimate the risk on preterm birth. Recently, a large number studies have reported near-perfect results distinguish between patients that will deliver term or using public resource, called Term/Preterm Electrohysterogram database. However, we argue these are o...

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 ...

A k nearest neighbor (kNN) classifier classifies a query instance to the most frequent class of its k nearest neighbors in the training instance space. For imbalanced class distribution, a query instance is often overwhelmed by majority class instances in its neighborhood and likely to be classified to the majority class. We propose to identify exemplar minority class training instances and gen...

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...

Classification plays a critical role in false positive reduction (FPR) in lung nodule computer aided detection (CAD). The difficulty of FPR lies in the variation of the appearances of the nodules, and the imbalance distribution between the nodule and non-nodule class. Moreover, the presence of inherent complex structures in data distribution, such as within-class imbalance and high-dimensionali...