Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/81434
Título: | Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing[Formula presented] |
Autor(es): | Matos, Luís Miguel Azevedo, João Matta, Arthur Pilastri, André Cortez, Paulo Mendes, Rui |
Palavras-chave: | CANE Data preprocessing Machine learning Python programming language |
Data: | 1-Ago-2022 |
Editora: | Elsevier 1 |
Revista: | Software Impacts |
Citação: | Matos, L. M., Azevedo, J., Matta, A., Pilastri, A., Cortez, P., & Mendes, R. (2022, August). Categorical Attribute traNsformation Environment (CANE): A python module for categorical to numeric data preprocessing. Software Impacts. Elsevier BV. http://doi.org/10.1016/j.simpa.2022.100359 |
Resumo(s): | Categorical Attribute traNsformation Environment (CANE) is a simpler but powerful data categorical preprocessing Python package. The package is valuable since there is currently a large range of Machine Learning (ML) algorithms that can only be trained using numerical data (e.g., Deep Learning, Support Vector Machines) and several real-world ML applications are associated with categorical data attributes. Currently, CANE offers three categorical to numeric transformation methods, namely: Percentage Categorical Pruned (PCP), Inverse Document Frequency (IDF) and a simpler One-Hot-Encoding method. Additionally, the CANE module is well documented with several code examples that can help in its adoption by non expert users. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/81434 |
DOI: | 10.1016/j.simpa.2022.100359 |
ISSN: | 2665-9638 |
Versão da editora: | https://www.sciencedirect.com/science/article/pii/S2665963822000720 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | CAlg - Artigos em revistas internacionais / Papers in international journals |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Categorical Attribute traNsformation Environment (CANE).pdf | 354,35 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons