This paper presents the performance of a classifier built using the stackingC algorithm in nine different data sets. Each data set is generated using a sampling technique applied on the original imbalanced data set. Five new sampling techniques are proposed in this paper (i.e., SMOTERandRep, Lax Random Oversampling, Lax Random Undersampling, Combined-Lax Random Oversampling Undersampling, and C...

-The class imbalanced problem occurs in various disciplines when one of target classes has a small number of instances compare to other classes. A classifier normally ignores or neglects to detect a minority class due to the small number of class instances. It poses a challenge to any classifier as it becomes hard to learn the minority class samples. Most of the oversampling methods may generat...

An approach to the construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification categories are not approximately equally represented. Often real-world data sets are predominately composed of “normal” examples with only a small percentage of “abnormal” or “interesting” examples. It is also the case that the cost of misclassifying an abnormal (i...

Real-world datasets are heavily skewed where some classes significantly outnumbered by the other classes. In these situations, machine learning algorithms fail to achieve substantial efficacy while predicting underrepresented instances. To solve this problem, many variations of synthetic minority oversampling methods (SMOTE) have been proposed balance which deal with continuous features. Howeve...

Objective: The traditional classifiers are ineffective in classifying the imbalanced datasets. Most popular approach resolving this problem is through data re-sampling. A hybrid resampling method proposed paper that reduces misclassification all classes. Method: employs Leader algorithm for under sampling and SMOTE oversampling. It generates desired number of samples both classes based on overc...

Data continuously gathered monitoring the spreading of COVID-19 pandemic form an unbounded flow data. Accurately forecasting if infections will increase or decrease has a high impact, but it is challenging because spreads and contracts periodically. Technically, data said to be imbalanced subject concept drifts signs decrements are minority class during periods, while they become majority in co...

Class imbalance is a frequent problem found in bioinformatics datasets. Unfortunately, the minority class is usually also the class of interest. One of the methods to improve this situation is data sampling. There are a number of different data sampling methods, each with their own strengths and weaknesses, which makes choosing one a difficult prospect. In our work we compare three data samplin...