Paper Publications
Date of Publication:2016-09-01Hits:
- Journal:IEEE Transactions on Knowledge and Data Engineering
- Abstract: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
- Indexed by:Journal paper
- Document Type:J
- Volume:28
- Issue:9
- Page Number:2438 - 2451
- Translation or Not:no
- Date of Publication:2016-09-01
- Included Journals:SCI
- Date of Publication:2016-09-01
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