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Table 1 The cost factors and data distribution in imbalanced retro-illumination images

From: Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network

Actual class Predicted class
Majority/negative Minority/positive
Majority/negative (1970) 0 1
Minority/positive (735) C min 0
  1. The numbers of positive and negative samples in the dataset were 735 and 1970, respectively. The cost factors for misclassifying positive and negative samples were C min and one respectively while the cost factor for correct classification was zero.