Abstract
AbstractGraph-based algorithms are known to be effective approaches to semi-supervised learning. However, there has been relatively little work on extending these algorithms to the multi-label classification case. We derive an extension of the Manifold Regularization algorithm to multi-label classification, which is significantly simpler than the general Vector Manifold Regularization approach. We then augment our algorithm with a weighting strategy to allow differential influence on a model between instances having ground-truth vs. induced labels. Experiments on four benchmark multi-label data sets show that the resulting algorithm performs better overall compared to the existing semi-supervised multi-label classification algorithms at various levels of label sparsity. Comparisons with state-of-the-art supervised multi-label approaches (which of course are fully labeled) also show that our algorithm outperforms all of them even with a substantial number of unlabeled examples.
Funder
Natural Sciences and Engineering Research Council of Canada
China Scholarship Council
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,Engineering (miscellaneous),Information Systems,Artificial Intelligence
Reference68 articles.
1. Ashfaq RAR, Wang XZ, Huang JZ, Abbas H, He YL (2017) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci 378:484–497
2. Belkin M, Niyogi P (2004) Semi-supervised learning on Riemannian manifolds. Mach Learn 56(1–3):209–239
3. Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 7(Nov):2399–2434
4. Belkin M, Niyogi P (2003) Using manifold structure for partially labeled classification. Adv Neural Inf Process Syst 953–960
5. Blum A, Chawla S (2001) Learning from labeled and unlabeled data using graph mincuts. In: Proc. 18th International Conf. on Machine Learning, pp 19–26
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