F-dist ## F k : A set of k-feature-sets ## R o : ν optimal rules (feature-sets) ## R k,ω : ω k-feature-sets for generating candidates ## selectNBest(R, n, S, Wr): n best rules from R ## with gain on {wi,r} m i=1 and training samples S procedure weak-learner(F k , S, W r ) ## ν best feature-sets as rules Figure 3: Find optimal feature-sets with given weights distributes features to subsets of features, called buckets, based on frequencies of features.
we guess training using a subset of features depends on how to distribute features to buckets like online learning algorithms that generally depend on the order of the training examples (Kazama and Torisawa, 2007).