Tri-Training algorithm in semi-supervised learning broke the restriction of previous algorithms on sufficient and redundant views and raised label efficiency by applying three classifiers to deal with labeled confidence.In order to further improve classifiers' performance through enhancing the difference between them

a semi-supervised collaborative classification algorithm with enhanced difference that applies three different classifiers was presented in this paper.Taking the might-be performance deterioration led by random sampling during the updating of classifying models into consideration

a method of stratified sampling based on labeled class was used by the algorithm to sample from the labeled sample sets

and the method of weighted voting based on classification accuracy realized the classifier ensemble

as a result the prediction accuracy is raised.Performance comparison between the proposed algorithm and Tri-Training algorithm was made through experiments

and the results show effectiveness of the former.