[Retracted] Active Learning for Imbalanced Data: The Difficulty and Proportions of Class Matter

Algorithm 1

Imbalanced active learning.

1: Input:

     Unlabeled samples , initially labeled samples , batch size ,

     parameters , maximum iteration number

2: Output:

    Model parameters

3: Initialize with

4: while not reach maximum iteration do

5:  Compute (the uncertainty of instance) based on (1)

6:  Compute (the difficulty of each class) based on (2), (3)

7:  Compute (the proportion of each class) based on (4)

8:  Compute (the query number of each class) based on (5), (6)

9:  Query instances from the set of each class in the descending order according to

10:  for each class do

11:   query instances with the lowest for labeling

    and add to with its annotation

12:  end for

13:  Update with

14: end while

15: return Model parameters