Klasy imbalance występują, gdy certain classes in a dataset are significationty undercompatited too other. This imbalance can lead to poor model performance, especialle in tasks like classificationn when e minority classes are critical. Adressising class imbalance iessential for developing g closate and reliable neurable networks.

Techniki Common to Adresaci Klasy Imbalance

Several methods are used to liquid class imbalance in neural networks. These techniques can be applied individually or combined for better results.

Methods data- Level

Datalevel approaches modify the dataset to balance class distribution. These include:

  • Xi1; Xi1; FLT: 0 Xi3; Xi3; Oversampling: Xi1; Xi1; FLT: 1 Xi3; Xi3; Vygasing thee e number of minority class samples, often thriptatung or synthetic data generation.
  • Xi1; Xi1; FLT: 0 Xi3; Xi3; Undersampling: Xi1; Xi1; FLT: 1 Xi3; Xi3; Reducing the number of majority class samples to match minority classes.
  • Xi1; Xi1; FLT: 0 Xi3; Xi3; SMOTE: Xi1; Xi1; FLT: 1 Xi3; Xi3; Synthetic Minority Over- sampling Technique creates new synthetic examples for minority classes.

Algorithm- Level Techniques

Tese metody modyfikują te algorytmy, które uczą się algorytmu tego, co lepiej zrobić, aby nie naruszać danych.

  • BL1; BLT: 0 BL3; BL3; BL1; BLT: 1 BL3; BLT: 0 BLT: 0 BL3; BL3; BLS: BLS: BLS: 0 BLS: 0 BLS: 3; BLS; BLS: 0 BLS: 3; BLS; BLS: BLS: BLS: 0 BLS: 3; BLS: BLS: 0 BLS: 3; BLS: 3; BLS: BLS: 0 BLS: 3; BLS: 3; BLLS: 0 BLLS: 0; BLLS: 0: BLS: BLS: BLS: BLS: 0: BLS: BLS: BLS: BLS: 0; BLS: BLS: 0: BLS: BLS: LS: BLS: LS: LS: LS: LS: LS: LS: LS: LS
  • FLT: 0 Xi3; FLT: 0 Xi3; FCL: Xi1; FLT: 1 Xi3; FLT: 1 Xi3; FLT: 0 Xi3; FLT: 0 Xi3; FLT: Xi1; FLT: Xi1; FLT: Xi1; FLT: Xi1; FLT: Xi1; FLT: Xi1; FLT: XI1; FLT: 0 XIX3; FLT: 0 X3; FLT: X3; FLT: X3; FLT: 0 XIX3; FLS: X3; FLT: XL: XIXL: XL: XL: XL: XL: XIXL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL: XL:

Przykłady Data Real- WorldData

In medical diagnoses, datasets often contain fewer positiva cases. Appliing oversampling or SMOTE can in improwize model sensitivity. In fraud detection, when e defraulent transactions are rare, cost-sensitivy learning helps thee model prioritize identifying these case effectively.