English
资源说明：Most current work onclassification hasbeen focused on learningfrom
a set of instances that are associated with a single label (i.e., single-label classi-
fication). However, many applications, such as gene functional prediction and
text categorization, may allow the instances to be associated with multiple la-
bels simultaneously. Multi-label classification is a generalization of single-label
classification, and its generality makes it much more difficult to solve.
Despiteitsimportance, researchonmulti-labelclassificationisstilllacking.Com-
mon approaches simply learn independent binary classifiers for each label, and
do not exploit dependencies among labels. Also, several small disjuncts may ap-
pear due to the possibly large number of label combinations, and neglecting these
small disjuncts may degrade classification accuracy. In this paper we propose a
multi-label lazy associative classifier, which progressively exploits dependencies
among labels. Further, since in our lazy strategy the classification model is in-
duced on an instance-based fashion, the proposed approach can provide a better
coverage of small disjuncts. Gains of up to 24% are observed when the proposed
approach is compared against the state-of-the-art multi-label classifiers.
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