Semi-Supervised Ensemble Classifier with Improved Sparrow Search Algorithm and Its Application in Pulmonary Nodule Detection

Algorithm 2

AdaBoost-ISSA-S4VM classification model algorithm.

	Input: weak classifier type: S4VM; train data set, train label set, test data set, test label set; the maximum iterations: ; kernel: ; parameters of S4VM: weight for the hinge loss of labeled instance , weight for the hinge loss of unlabeled instance , and the sampling times for each trial .
	Output: prediction label of test data set
(1)	set the weights of the training data set , ;
(2)	for
(3)	If there are misclassification points
	/ Parameter selection based on SCA-CSSA /
(4)	According to SCA-CSSA, find the optimal hyper-parameters of weak classifier S4VM;
	/Weight of AdaBoost selection based on SCA-CSSA /
(5)	According to SCA-CSSA, find the optimal weight of weak classifier S4VM;
(6)	Using the weight distribution , calculate the weak classifier ;
(7)	Update the weight distribution of the training set ;
(8)	;
(9)	else
(10)	jump out of the loop;
(11)	end
(12)	end for
(13)	According to formula (11), groups of weak classifiers are linearly combined, and the final classifier is output;
(14)	Use the final classifier to predict the training set classification.