ML Anthology

Distinct Chains for Different Instances: An Effective Strategy for Multi-Label Classifier Chains

Pablo Nascimento da Silva, Eduardo Corrêa Gonçalves, Alexandre Plastino, Alex Alves Freitas
ECML-PKDD 2014 pp. 453-468
doi:10.1007/978-3-662-44851-9_29 /ecmlpkdd/2014/dasilva2014ecmlpkdd-distinct/

Abstract

Multi-label classification (MLC) is a predictive problem in which an object may be associated with multiple labels. One of the most prominent MLC methods is the classifier chains (CC). This method induces q binary classifiers, where q represents the number of labels. Each one is responsible for predicting a specific label. These q classifiers are linked in a chain, such that at classification time each classifier considers the labels predicted by the previous ones as additional information. Although the performance of CC is largely influenced by the chain ordering, the original method uses a random ordering. To cope with this problem, in this paper we propose a novel method which is capable of finding a specific and more effective chain for each new instance to be classified. Experiments have shown that the proposed method obtained, overall, higher predictive accuracies than the well-established binary relevance, CC and CC ensemble methods.

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Cite

Text

da Silva et al. "Distinct Chains for Different Instances: An Effective Strategy for Multi-Label Classifier Chains." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44851-9_29

Markdown

[da Silva et al. "Distinct Chains for Different Instances: An Effective Strategy for Multi-Label Classifier Chains." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/dasilva2014ecmlpkdd-distinct/) doi:10.1007/978-3-662-44851-9_29

BibTeX

@inproceedings{dasilva2014ecmlpkdd-distinct,
  title     = {{Distinct Chains for Different Instances: An Effective Strategy for Multi-Label Classifier Chains}},
  author    = {da Silva, Pablo Nascimento and Gonçalves, Eduardo Corrêa and Plastino, Alexandre and Freitas, Alex Alves},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2014},
  pages     = {453-468},
  doi       = {10.1007/978-3-662-44851-9_29},
  url       = {https://mlanthology.org/ecmlpkdd/2014/dasilva2014ecmlpkdd-distinct/}
}