Learning from imbalanced data is one of the burning issues of the era. Traditional classification methods exhibit degradation in their performances while dealing with imbalanced data sets due to skewed distribution of data into classes. Among various suggested solutions, instance based weighted approaches secured the space in such cases. In this paper, we are proposing a new fuzzy weighted near...

Machine learning technology has a massive impact on society because it offers solutions to solve many complicated problems like classification, clustering analysis, and predictions, especially during the COVID-19 pandemic. Data distribution in machine been an essential aspect providing unbiased solutions. From earliest literatures published highly imbalanced data until recently, research focuse...

As growing numbers of real world applications involve imbalanced class distribution or unequal costs for misclassification errors in different classes, learning from imbalanced class distribution is considered to be one of the most challenging issues in data mining research. This study empirically investigates the sensitivity of bagging predictors with respect to 12 algorithms and 9 levels of c...