A Novel Key Influencing Factors Selection Approach of P2P Lending Investment Risk

Algorithm 1

The key factors selection approach.

Inputs: the initial parameters, the initial data of P2P lending, and MFD computing system.
Outputs: the key influencing factors of P2P lending .
(1)	Initialize the parameters.
(2)	glowworms are generated randomly, and compute their MFD using equation (14).
(3)	, .
(4)	.
(5)	while do
(6)	for to do
(7)	Select the objective glowworm in the radial range local-decision domain of the glowworm .
(8)	Move a step to using equations (6)–(9).
(9)	Update the luciferin and the radial range local-decision domain .
(10)	if do
(11)	glowworms are divided into three subpopulations according to their MFD.
(12)	Perform the coevolution mechanism to create offspring glowworms and update their parent glowworms.
(13)	end if
(14)	if do
(15)	Perform the fireworks evolution strategy to create new glowworms and update the current glowworm.
(16)	end if
(17)	end for
(18)	, .
(19)	end while
(20)	Obtain the preliminary attribute subset which corresponds to .
(21)	Get the attribute subset by eliminating those attributes that are not significantly related to the default risk in using the probit regression.
(22)	Form an attribute subset extracted from the original dataset of P2P lending using the artificial prior knowledge.
(23)	Generate a small and reasonable number of attribute subsets by adding the attributes in into .
(24)	Get the classification accuracies by evaluating each subset in using ELM.
(25)	Achieve the key influencing factors of P2P lending with the highest classification accuracy.
(26)	return