Research Article

[Retracted] Analysis of Risk Factors of Neurobiological Pipeline Care and Investigation of Preventive Measures

Table 2

SMOTEC-SMO algorithm descriptions.

Input: Number of minority class samples N; the number of clusters K; similarity threshold S; SMOTE oversampling expansion multiplier T%.
Output: SMO classification plane and prediction results.
Initialize the hypermastigote, which include the number of iterations t, the learning rate L, the hypermastigote of the recurrent neural network , , and the computational gradient of the model ;
(1) BEGIN/ Improved SMOTE Over-sampling/
(2) G = int(NT%); //Oversampling a few class examples
(3) For i = 1 to G
(4) For I = 1 to N
(5) Compute k-NN ; //Find 5 nearest neighbors of
(6) For i = 1 to nuberattr/∗nuberattrIndicates the number of attributes/
(7) If is a numeric attribute
(8) If
(9) Rand(1)//Different generation methods for different cases of near neighbors
(10) elseif
(11) rand(0.5)
(12) endif
(13) endif
(14) endFor
(15) endFor
(16) endFor
(17)
(18) If , / If j is a continuous value /
(19) If If J is a discrete value / Clustering under-samping /
(20) Clustering divides the training data set into K clusters;
/ SMO /SMOTraining; //Training with sequential minimum optimization (SMO) algorithm
SMOProjections;
END