Research on Credit Risk Prediction under Unbalanced Dataset Based on Ensemble Learning

Algorithm 1

bmRelief algorithm.

	Input: dataset D = {(, )}i^n, xi ∈ R^T, random sampling times N, feature weight threshold β;
	Output: the filtered variable feature set S
(1)	Set the feature weight to 0, F = True, and the set S is empty
(2)	for i = 1 to N do
(3)	if F == True
	Randomly draw a sample xi from the minority sample, F = False
(4)	else
	A sample is randomly selected from the majority of samples, F = True
(5)	Use formulas (4) and (5) to calculate the probability that xi is used to update feature weights
(6)	Generate a random number from 0 to 1 μ
(7)	if μ <
	Find the nearest neighbor sample H from the similar samples of , and find the nearest neighbor sample M from different classes
	for i = 1 to T do
	Update the feature weight according to formula (1)
(8)	for i = 1 to T do
	if  ≥ β
	Add the i-th feature to the set S
(9)	return S