Data skewness continues to be one of the leading factors which adversely impacts machine learning algorithms performance. An approach reduce this negative effect data variance is pre-process former dataset with level resampling strategies. Resampling strategies have been seen in two forms, oversampling and undersampling. strategy proposed article for tackling multiclass imbalanced datasets. Thi...

Indonesia has considerable tourism development potential, this phenomenon is in accordance with the number of foreign tourist visits to from January September 2022 recorded by Badan Pusat Statistik many as 2,397,181 visitors. This research focuses on super-priority destinations Labuan Bajo, East Nusa Tenggara, based government's plan that focus developing destination increase hotel meet need fo...

Traditional supervised machine learning classifiers are challenged to learn highly skewed data distributions as they designed expect classes equally contribute the minimization of cost function. Moreover, design expects equal misclassification costs, causing a bias for overrepresented classes. Different strategies have been proposed correct this issue. The modification set has become common pra...

There is a perpetual elevation in demand for higher education in the last decade all over the world; therefore, the need for improving the education system is imminent. Educational data mining is a newly-visible area in the field of data mining and it can be applied to better understanding the educational systems in Bangladesh. In this research, we present how data can be preprocessed using a d...

Cashless transactions such as online transactions, credit card transactions, and mobile wallet are becoming more and more popular in financial transactions nowadays. With increased number of such cashless transaction, fraudulent transactions are also increasing. Fraud can be detected by analyzing spending behavior of customers (users) from previous transaction data. If any deviation is noticed ...

Data in many biological problems are often compounded by imbalanced class distribution. That is, the positive examples may largely outnumbered by the negative examples. Many classification algorithms such as support vector machine (SVM) are sensitive to data with imbalanced class distribution, and result in a suboptimal classification. It is desirable to compensate the imbalance effect in model...

The low and high arrhythmic risk of myocardial infarction is classified based on size, location, and textural information of scarred myocardium. These features are extracted from late gadolinium (LG) enhanced cardiac magnetic resonance images (MRI) of post-MI patients. The risk level caused by features are evaluated by using various classifiers including k-nearest neighbor (k-NN), support vecto...

In this thesis we study the classification task in the presence of class imbalanced data. This task arises in many applications when we are interested in the under-represented (minority) classes. Examples of such applications are related to fraud detection, medical diagnosis and monitoring, text categorization, risk management, information retrieval and filtering. Although there exist many stan...