| Autor(es): |
Morales, Pablo
;
Luengo, Julian
;
Garcia, Luis P. F.
;
Lorena, Ana C.
;
de Carvalho, Andre C. P. L. F.
;
Herrera, Francisco
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | R JOURNAL; v. 9, n. 1, p. 219-228, JUN 2017. |
| Citações Web of Science: | 3 |
| Resumo | |
In Data Mining, the value of extracted knowledge is directly related to the quality of the used data. This makes data preprocessing one of the most important steps in the knowledge discovery process. A common problem affecting data quality is the presence of noise. A training set with label noise can reduce the predictive performance of classification learning techniques and increase the overfitting of classification models. In this work we present the NoiseFiltersR package. It contains the first extensive R implementation of classical and state-of-the-art label noise filters, which are the most common techniques for preprocessing label noise. The algorithms used for the implementation of the label noise filters are appropriately documented and referenced. They can be called in a R-user-friendly manner, and their results are unified by means of the ``filter{''} class, which also benefits from adapted print and summary methods. (AU) | |
| Processo FAPESP: | 12/22608-8 - Uso de medidas de complexidade de dados no suporte ao aprendizado de máquina supervisionado |
| Beneficiário: | Ana Carolina Lorena |
| Modalidade de apoio: | Auxílio à Pesquisa - Jovens Pesquisadores |
| Processo FAPESP: | 13/07375-0 - CeMEAI - Centro de Ciências Matemáticas Aplicadas à Indústria |
| Beneficiário: | Francisco Louzada Neto |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |
| Processo FAPESP: | 11/14602-7 - Detecção e eliminação de ruídos para problemas de classificação |
| Beneficiário: | Luís Paulo Faina Garcia |
| Modalidade de apoio: | Bolsas no Brasil - Doutorado Direto |