Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/66817
Título: | Chemical laboratories 4.0: A two-stage machine learning system for predicting the arrival of samples |
Autor(es): | Silva, António João Cortez, Paulo Pilastri, André |
Palavras-chave: | Automated Machine Learning Industry 4.0 Regression |
Data: | 2020 |
Editora: | Springer |
Revista: | IFIP Advances in Information and Communication Technology |
Resumo(s): | This paper presents a two-stage Machine Learning (ML) model to predict the arrival time of In-Process Control (IPC) samples at the quality testing laboratories of a chemical company. The model was developed using three iterations of the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, each focusing on a different regression approach. To reduce the ML analyst effort, an Automated Machine Learning (AutoML) was adopted during the modeling stage of CRISP-DM. The AutoML was set to select the best among six distinct state-of-the-art regression algorithms. Using recent real-world data, the three main regression approaches were compared, showing that the proposed two-stage ML model is competitive and provides interesting predictions to support the laboratory management decisions (e.g., preparation of testing instruments). In particular, the proposed method can accurately predict 70% of the examples under a tolerance of 4 time units. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/66817 |
ISBN: | 9783030491857 |
DOI: | 10.1007/978-3-030-49186-4_20 |
ISSN: | 1868-4238 |
Versão da editora: | https://link.springer.com/chapter/10.1007%2F978-3-030-49186-4_20 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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aiai44.pdf | 282,94 kB | Adobe PDF | Ver/Abrir |