Over the last few decades, credit card fraud (CCF) has been a severe problem for both cardholders and providers. Credit transactions are fast expanding as internet technology advances, significantly relying on internet. With advanced increased usage, rates becoming economy. However, dataset is highly imbalanced skewed. Many classification techniques used to classify non-fraud but in certain con...

Owning life insurance coverage that is not enough to pay for the expenses called underinsurance, and it has been found have a significant influence on sustainability financial health of families. However, companies need good profile potential policyholders. Customer profiling become one essential marketing strategies any sustainable business, such as market, identify purchasers. One well-known ...

In supervised machine learning, class imbalance is commonly occurring when the number of examples that represent one much lower than other classes. Since an data may generate suboptimal classification models, it could lead to minority are misclassified frequently and hardly achieving best performance. This study proposes improved support vector (SVM) method for imbalanced namely as SVM-GA by op...

Aiming at the imbalance problem of wireless link samples, we propose quality estimation method which combines K-means synthetic minority over-sampling technique (K-means SMOTE) and weighted random forest. The adopts mean, variance asymmetry metrics physical layer parameters as parameters. is measured by level determined packet receiving rate. used to cluster samples. SMOTE employed synthesize s...

Traditional firewalls and data encryption techniques can no longer match the demands of current IoT network security due to rising amount variety threats. In order manage risks, intrusion detection solutions have been advised. Even though machine learning (ML) helps widely used currently in use, these algorithms struggle with low rates requirement for extensive feature engineering. The deep mod...

Learning from imbalanced datasets is inherently difficult due to lack of information about the minority class. In this paper, we study the performance of SVMs, which have gained great success in many real applications, in the imbalanced data context. Through empirical analysis, we show that SVMs suffer from biased decision boundaries, and that their prediction performance drops dramatically whe...

Mobile Money Fraud is advancing in developing countries. We propose a solution to this problem based on machine learning. Labeled data from financial transactions which include mobile money are, however, skewed towards the negative class. Machine learning models built with such datasets are unreliable as prediction algorithms will be biased investigate performance of different sampling and weig...

Patent images are very important for patent examiners to understand the contents of an invention. Therefore there is a need for automatic labelling of patent images in order to support patent search tasks. Towards this goal, recent research works propose classification-based approaches for patent image annotation. However, one of the main drawbacks of these methods is that they rely upon large ...

Software defects are one of the main contributors to information technology waste and lead rework, thus consuming a lot time money. defect prediction has objective prevention by classifying certain modules as defective or not defective. Many researchers have conducted research in field software using NASA MDP public datasets, but these datasets still shortcomings such class imbalance noise attr...

Imbalanced class has been a common problem encountered in the modeling process, and attracted more attention from scholars. Biased classifiers, which limit classifiers' performance for minority classes, will be produced if imbalanced ratio between number of positive labels negative is ignored. The synthetic over-sampling technique (SMOTE) very classic popular method, widely used to address this...