Full Model Selection in Big Data
| Autor: | Hugo Jair Escalante-Balderas, Carlos A. Reyes-García, Angel Díaz-Pacheco, Jesús A. Gonzalez-Bernal |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
Computer science Model selection Big data Context (language use) 02 engineering and technology Machine learning computer.software_genre Asset (computer security) Task (project management) Quantities of information Order (exchange) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Selection (genetic algorithm) |
| Zdroj: | Advances in Soft Computing ISBN: 9783030028367 MICAI (1) |
| Popis: | The increasingly larger quantities of information generated in the world over the last few years, has led to the emergence of the paradigm known as Big Data. The analysis of those vast quantities of data has become an important task in science and business in order to turn that information into a valuable asset. Many data analysis tasks involves the use of machine learning techniques during the model creation step and the goal of these predictive models consists on achieving the highest possible accuracy to predict new samples, and for this reason there is high interest in selecting the most suitable algorithm for a specific dataset. This trend is known as model selection and it has been widely studied in datasets of common size, but poorly explored in the Big Data context. As an effort to explore in this direction this work propose an algorithm for model selection in Big Data. |
| Databáze: | OpenAIRE |
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