Pavement performance models that can model consistently well across different pavement types, structures, traffic, and environmental conditions as throughout pavements’ lives are essential for the efficient management of transportation infrastructures. However, network data inherently imbalanced, most built on algorithms assume balanced distribution equal error cost. As a result, may vary group...

The number of financial transactions has the potential to cause many violations law (fraud). Conventional machine learning been widely used, including logistic regression, random forest, and gradient boosted. However, can work as long dataset contains fraud. Many new technology companies need anticipate for fraud, which they have not experienced much. This a crime also be by old service provide...

Prediction and correct voting is critical task in imbalance data multi-class classification. Accuracy and performance of multi-class depends on voting and prediction of new class data. Assigning of new class of imbalance data generate confusion and decrease the accuracy and performance of classifier. Various authors and research modified the multiclass classification approach such as one agains...

The research of imbalance data classification is the hot point in the field of data mining. Conventional classifiers are not suitable to the imbalanced learning tasks since they tend to classify the instances to the majority class which is the less important class. This paper pays close attention to the uniqueness of uneven data distribution in imbalance classification problems. Without change ...