Co-training algorithm is constrained by its assumption that the features can be split into two compatible and independent subsets.However

the assumption is usually violated in real-world application

especially for independence.We discover its real purpose is to find two classifiers with certain accuracy and sufficient diversity to co-train.First

multi-views are created using different term evaluation functions.Second

instead of directly computing the independence between two sub-views

this paper evaluates the independence between two classifiers

trained on them

by using diversity measures indirectly.Thus a pair of classifiers with certain accuracy and greater diversity is selected.The experimental results show two improved algorithms named TV-SC and TV-DC are both outperform another co-training algorithm named Co-Rnd based on random splitting method

and TV-DC outperforms TV-SC.