| Texto completo | |
| Autor(es): |
Costa Junior, Joel D.
;
Faria, Elaine R.
;
Silva, Jonathan A.
;
Gama, Joao
;
Cerri, Ricardo
;
IEEE
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2019 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 8-pg., 2019-01-01. |
| Resumo | |
In multi-label classification problems an example can be simultaneously classified into more than one class. This is also a challenging task in Data Streams (DS) classification, where unbounded and non-stationary distributed multi-label data contain multiple concepts that drift at different rates and patterns. In addition, the true labels of the examples may never become available and updating classification models in a supervised fashion is unfeasible. In this paper, we propose a Multi-Label Stream Classification (MLSC) method applying a Novelty Detection (ND) procedure task to update the classification model detecting any new patterns in the examples, which differ in some aspects from observed patterns, in an unsupervised fashion without any external feedback. Although ND is suitable for multi-class stream classification, it is still a not well-investigated task for multi-label problems. We improve a initial work proposed in [1] and extended it with a new Pruned Sets (PS) transformation strategy. The experiments showed that our method presents competitive performances over data sets with different concept drifts, and outperform, in some aspects, the baseline methods. (AU) | |
| Processo FAPESP: | 17/11513-0 - Classificação multirrótulo em fluxo contínuo de dados |
| Beneficiário: | Joel David Costa Júnior |
| Modalidade de apoio: | Bolsas no Brasil - Mestrado |
| Processo FAPESP: | 18/11321-6 - Avaliação de algoritmos de detecção de novidade para classificação multirrótulo em fluxos contínuos de dados |
| Beneficiário: | Joel David Costa Júnior |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Mestrado |