[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