XGBoost is one of the popular machine learning models used in the domains like fraud
detection as well as to tackle the class imbalance that creates overfitting if not handled …

Real-time crash risk prediction models aim to identify pre-crash conditions as part of active
traffic safety management. However, traditional models which were mainly developed …

Due to rapid enhancement in technology, several problems have been raised in computer
science (in the previous decade) like Class imbalance problem, duplicity of data in cloud …

In the past decade, machine learning techniques have been used for solving several
problems with respect to big data. In current, there are several types of Machine Learning …

The emergence of large amounts of hesitant fuzzy data brings more opportunities and
challenges for optimal decision-making results. The granularity of the hesitant fuzzy set has …

In numerous real-world applications/domains, the class imbalance problem is prevalent/hot
topic to focus. In various existing work, for solving class imbalance problem, almost data is …

Imbalanced datasets typically make prediction accuracy difficult. Most of the real-world data
are imbalanced in nature. The traditional classifiers assume a well-balanced class …

Class imbalance has been widely accepted as a significant factor that negatively impacts a
machine learning classifier's performance. One of the techniques to avoid this problem is to …

Due to the imbalanced data of outnumbered legitimate transactions than the fraudulent
transaction, the detection of fraud is a challenging task to find an effective solution. In this …

Most of the traditional classification algorithms assume their training data to be well-
balanced in terms of class distribution. Real-world datasets, however, are imbalanced in …