Abstract This study aims to improve the accuracy of forecasting turnover intention new college graduates by solving imbalance data problem. For this purpose, from Korea Employment Information Service's Job Mobility Survey (Graduates Occupations Survey: GOMS) for were used. includes various items such as intention, personal characteristics, and job characteristics graduates, class ratio is imbal...

In this work, the problem of anomaly detection in imbalanced datasets, framed context network intrusion is studied. A novel solution that takes both data-level and algorithm-level approaches into account to cope with class-imbalance proposed. This integrates auto-learning ability Reinforcement Learning oversampling a Conditional Generative Adversarial Network (CGAN). To further investigate pote...

Various approaches to extend bagging ensembles for class imbalanced data are considered. First, we review known extensions and compare them in a comprehensive experimental study. The results show that integrating bagging with under-sampling is more powerful than over-sampling. They also allow to distinguish Roughly Balanced Bagging as the most accurate extension. Then, we point out that complex...

Abstract Machine learning plays an increasingly significant role in the building of Network Intrusion Detection Systems. However, machine models trained with imbalanced cybersecurity data cannot recognize minority data, hence attacks, effectively. One way to address this issue is use resampling, which adjusts ratio between different classes, making more balanced. This research looks at resampli...

Because the numbers of cars reflect each person's travel behaviors for specific location, car ownership demand model plays a dominant role in analysis order to understand area's individual and household behaviors. However, study project master plan Khon Kaen expressway represented imbalanced data; namely, majority class minority were not equal. Before developing machine learning model, this sug...

Many real world data mining applications involve learning from imbalanced data sets. Learning from data sets that contain very few instances of the minority (or interesting) class usually produces biased classifiers that have a higher predictive accuracy over the majority class(es), but poorer predictive accuracy over the minority class. SMOTE (Synthetic Minority Over-sampling TEchnique) is spe...

The healthcare sector has traditionally been an early use of technological progress and achieved significant advantages, especially in the field machine learning like prediction diseases. COVID-19 epidemic is still having impact on every facet life necessitates a fast accurate diagnosis. Early detection exceptionally critical to saving lives human beings. need for effective, rapid, precise way ...

The importance of Land Cover (LC) classification is recognized by an increasing number scholars who employ LC information in various applications (i.e., address global climate change and achieve sustainable development). However, studying the roles balancing data, image integration, performance different machine learning algorithms landscapes has not received as much attention from scientists. ...