Rozwiązanie problemów nierównowagi klas w głębokim uczenia się poprzez pobieranie próbek danych i uczenie się w sposób wygodny z kosztami

Klasy imbalance is a considence in deep ech learning where some classes have signitantly fewer examples than others. Thi imbalance can lead to biased models that perfom poorly on minority classes. Adressing this issue involves techniques such as data sampling and cost- sensitivy learning.

Data Sampling Techniques

Data sampling dostosowuje te dystrybucje do poziomu danych to balance class reprezentatywna. Kommon metodyki obejmują oversampling minority classes and undersampling majority classes. These techniques help thee model learn equally from all classes.

Cost- sensitiva Learning

Cost- sensitivie learning assigns higher misclassification costs to o minurity classes. Thi approach conforges the model to pay more attention to underconseted classes during training, improwing overall performance on imbalanced datasets.

Wdrożenie strategii

Effective strategies included combinang data sampling wigh cost- sensitive loss functions. Additionally, techniques like focal loss can focus training our hard-to-classify examples, further limplicatg class imbalance issues.