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• W2951493960 abstract "The process of extracting knowledge from natural language text poses a complex problem that requires both a combination of machine learning techniques and proper feature selection. Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the most suitable combination of them. This paper proposes a novel AutoML strategy based on probabilistic grammatical evolution, which is evaluated on the health domain by facing the knowledge discovery challenge in Spanish text documents. Our approach achieves state-of-the-art results and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. Source code is provided for the research community." @default.
• W2951493960 created "2019-06-27" @default.
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• W2951493960 date "2019-01-01" @default.
• W2951493960 modified "2023-09-23" @default.
• W2951493960 title "AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text" @default.
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• W2951493960 doi "https://doi.org/10.18653/v1/p19-1428" @default.
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