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发表刊物：IEEE Transactions on Knowledge and Data Engineering

摘要：Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditi

论文类型：期刊论文

文献类型：J

卷号：28

期号：9

页面范围：2438 - 2451

是否译文：否

发表时间：2016-09-01

收录刊物：SCI