Megjelöltújramintavétel

Megjelöltújramintavétel, which translates to "marked resampling" in English, is a technique used in statistical sampling and machine learning. It is employed when dealing with imbalanced datasets, where one class or category has significantly fewer instances than others. The core idea is to oversample the minority class by replicating its existing instances, but with a modification. Instead of simply duplicating the data points, marked resampling assigns a "mark" or weight to the resampled instances. This mark indicates that these instances are artificial or augmented, distinguishing them from the original data.

The purpose of this marking is to prevent certain machine learning algorithms from treating the resampled instances as equally valid as the original data. By assigning a lower weight or a specific flag, algorithms can be adjusted to be more cautious when learning from these synthetic data points. This helps to mitigate issues like overfitting, where a model becomes too specialized to the specific, often limited, characteristics of the minority class after oversampling.

Different methods exist for implementing marked resampling, often varying in how the "mark" is applied and how algorithms interpret it. The effectiveness of marked resampling depends on the specific dataset, the chosen machine learning algorithm, and the method of marking employed. It is a strategy to improve the performance of models on tasks where class imbalance is a significant challenge.