A Decoupling and Bidirectional Resampling Method for Multilabel Classification of Imbalanced Data with Label Concurrence

Algorithm 1

The pseudocode of ML-DBR.

	Algorithm ML-DBR:
	Input: A multilabel dataset D, resampling rate
	Output: Preprocessed dataset D
	Decoupling strategy
	(1)  Calculate samplesToResampling =  P, IR, Mean IR & Mean Samples
	(2)  Calculate SCUMBLEIns in D and set the SCUMBLE(D) as SCUMBLE(D)1
	(3)  For in D,
	(4)    If , then
	(5)      clone ( is the label set of )
	(6)       ( is the decoupled dataset)
	(7)        For samples
	(8)          Recalculate the SCUMBLE(D) as SCUMBLE(D)j
	(9)          If SCUMBLE(D)j−1 –SCUMBLE(D)j < t, then stop decoupling
	(10)  D = D +
	Resampling strategy
	(11)  While samplesToResampling > 0
	(12)    random select
	(13)    if then
	(14)
	(15)      While
	(16)        random get m samples from
	(17)        let the max Min-SCUMBLEIns sample of samples as Z, clone Z
	(18)        D = D + Z, , samplesToResampling
	(19)    if then
	(20)
	(21)      While
	(22)        random get m samples from
	(23)        Let the max Min-SCUMBLEIns sample of m samples as Z, Set of Z to 0
	(24)        , samplesToResampling
	(25)    Recalculate MeanIR, if then stop algorithm
	(26)  return D