[Retracted] Application of Artificial Intelligence Combined with 5G Technology in the Reform of English Teaching in Universities

Algorithm 1

Random forest classification algorithm

(i)	Input:
(ii)	S: the trained set of information
(iii)	B: characteristic set
(iv)	Z: characteristic set
(v)	l: Number of trees
(vi)	Output: Random Forest R
(vii)	Technique:
(viii)	For i = 1 to l do
(ix)	From the training information group S, create a bootstrap specimen in-of-bag data group IOBi and out-of-bag data group OOBi;
(x)	ti(IOBi) = generateTree(IOBi);
(xi)	formula (12) is used to compute the OOBRi of the ti(IOBi) using the OOBi;
(xii)	End for
(xiii)	Arrange all l in order of their OOBR;
(xiv)	Choose the best 85 percent of trees with strong OOBR scores and merge the 85 percentage l into a better random forest R;
(xv)	generateTree()
(xvi)	Make the latest node;
(xvii)	If the halting criteria are fulfilled, then
(xviii)	Returnas a leaf node;
(xix)	Else
(xx)	For j = 1 to N do
(xxi)	Calculate the parameter cor(Bj, Z) by equation (10);
(xxii)	End for
(xxiii)	Estimateby formula (11);
(xxiv)	Utilize the feature weighting technique;
(xxv)	Characteristics (c) are utilized as options for generating the optimum partitioning division for the node;
(xxvi)	For each partition, perform generateTree ();
(xxvii)	End if
(xxviii)	Return G;