An Improved Ensemble Based Technique for Handling Noisy ‎Class Imbalnced Education Data for Prediction of Students ‎Dropout in Hei - Sangeetha , Ms. K.; Shanmugapriya, Dr. N. (International Journal of Basic and Applied Sciences, 2025)
Increasingly, the sector of education is becoming more interested in the creation of intelligent technology. The fast rise of educational data ‎suggests that standard processing methods may have limitations and may even result in distortion. It is for this reason that the process of ‎reconstructing the research technique of data mining in the field of education has become increasingly important. The amount of information ‎about students that is stored in educational databases is growing on a daily basis; thus, the information that is extracted from these databases ‎needs to be updated on a consistent basis. In a scenario in which there is a requirement to manage a constant flow of student data, there is a ‎challenge of figuring out how to manage this enormous volume of data into the information and how to adapt new knowledge that is ‎introduced with the new data. When working with classes that have few instances, a class imbalance issue is crucial. The machine learning ‎classification of classes is significantly impacted by noisy, class-unbalanced datasets. This research proposes an enhanced hybrid bag-boost ‎model using a suggested resampling technique. A suggested resampling method for addressing noisy, unbalanced datasets is included in this ‎model. The suggested resampling method includes Edited Nearest Neighbor (ENN) and K-Means SMOTE (Synthetic Minority ‎Oversampling Technique) as an oversampling method. The technique of Undersampling is employed to eliminate noise. Three levels of ‎noise reduction are achieved with this resampling technique: first, datasets are clustered using the K-Means clustering technique; second, ‎imbalance is handled by SMOTE inside clusters, which introduces synthetic instances of the class in the minority; and third, instances that ‎generate noise are removed using the ENN technique. The suggested model outperforms the others, according to experimental data. ‎Furthermore, it has been verified that the suggested method works better in binary unbalanced datasets when the noise proportion is raised‎.