| Texto completo | |
| Autor(es): |
Tomas, Jimena Torres
;
Spolaor, Newton
;
Cherman, Everton Alvares
;
Monard, Maria Carolina
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | ELECTRONIC NOTES IN THEORETICAL COMPUTER SCIENCE; v. 302, p. 22-pg., 2014-02-25. |
| Resumo | |
A controlled environment based on known properties of the dataset used by a learning algorithm is useful to empirically evaluate machine learning algorithms. Synthetic (artificial) datasets are used for this purpose. Although there are publicly available frameworks to generate synthetic single-label datasets, this is not the case for multi-label datasets, in which each instance is associated with a set of labels usually correlated. This work presents Mldatagen, a multi-label dataset generator framework we have implemented, which is publicly available to the community. Currently, two strategies have been implemented in Mldatagen: hypersphere and hypercube. For each label in the multi-label dataset, these strategies randomly generate a geometric shape (hypersphere or hypercube), which is populated with points (instances) randomly generated. Afterwards, each instance is labeled according to the shapes it belongs to, which defines its multi-label. Experiments with a multi-label classification algorithm in six synthetic datasets illustrate the use of Mldatagen. (AU) | |
| Processo FAPESP: | 11/12597-6 - Geração de Conjuntos de Dados Sintéticos para Aprendizado Multirrótulo |
| Beneficiário: | Jimena Torres Tomas |
| Modalidade de apoio: | Bolsas no Brasil - Iniciação Científica |
| Processo FAPESP: | 10/15992-0 - Explorando a dependência de rótulos no aprendizado multirrótulo |
| Beneficiário: | Everton Alvares Cherman |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado Direto |
| Processo FAPESP: | 11/02393-4 - Seleção de Atributos para Aprendizado Multirrótulo |
| Beneficiário: | Newton Spolaôr |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado |