文件名称：Multi-Label Lazy Associative Classification
文件大小：86KB
文件格式：PDF
更新时间：2019-03-22 04:00:40
Multi-Label
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.