We randomly divide the labeled data set into five folds so that each fold at least contains one example instance labeled by each attribute node.
to test which is the best to be employed by the proposed LFS framework, we further comparatively study five classic feature selection algorithms respectively based on document frequency (DF) (Manning et al., 2008), mutual information (MI) (Manning et al., 2008;Church and Hanks, 1990), χ 2statistic (CHI) (Manning et al., 2008), information gain (IG) (Mitchell, 1997), and term strength (t-S) (Wilbur and Sirotkin, 1992).