Making class balance is essential when learning from highly skewed datasets; otherwise, a learner may classify all instances to negative class, resulting in high false-negative rate. As result, precise balancing strategy required. Many researchers have investigated imbalance using Machine Learning (ML) methods due their powerful generalization performance and interpreting capabilities, comparin...

Up-sampling and down-sampling are the two most used methods in balancing the data when dealing with two class imbalance problem. However, none of the existing approaches to class rebalance take into account class information (e.g. distribution, within and between class distances, imbalance factor). This study presents initial results of up-sampling methods based on various approaches to aggrega...

In Data mining and Knowledge Discovery hidden and valuable knowledge from the data sources is discovered. The traditional algorithms used for knowledge discovery are bottle necked due to wide range of data sources availability. Class imbalance is a one of the problem arises due to data source which provide unequal class i.e. examples of one class in a training data set vastly outnumber examples...

The class imbalance problem causes a classier to overt the data belonging to the class with the greatest number of training examples. The purpose of this paper is to argue that methods that equalize class membership are not as e ective as possible when applied blindly and that improvements can be obtained by adjusting for the within-class imbalance. A guided resampling technique is proposed and...

The class imbalance problem occurs when one far outnumbers the other classes, causing most traditional classifiers perform poorly on minority classes. To tackle this problem, a plethora of techniques have been proposed, especially centered around resampling methods. This paper introduces two-stage method that combines DBSCAN clustering algorithm to filter noisy majority instances with graph-bas...