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• W2897567604 abstract "This work focuses on the application of an evolutionary fuzzy rule-based system following the MapReduce principle to real-world big data regression problems. The examined datasets originate from the field of soil spectroscopy and are high in volume, variety, and dimensionality. They are part of the GEO-CRADLE and LUCAS soil spectral libraries, which encompass large geographical areas with a significant amount of patterns. In this work, a two-tier MapReduce scheme is proposed. The first-tier defines the Data Base by selecting optimal granularities per each feature. The second-tier applies the DECO 3 RUM algorithm in a distributed way using bootstrapped samples to tackle the large inherent sparsity of the data. The resulting rule bases are aggregated using a final instance of DECO 3 RUM. The performance of the proposed methodology was analyzed and statistically compared to the serial version as well as a fully distributed version. It was demonstrated that the version using bootstrapped samples outperforms the other versions in terms of accuracy, while being as compact as the serial version; thus, providing a good balance between accuracy and execution speed." @default.
• W2897567604 created "2018-10-26" @default.
• W2897567604 creator A5005613465 @default.
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• W2897567604 date "2018-07-01" @default.
• W2897567604 modified "2023-09-25" @default.
• W2897567604 title "An evolutionary fuzzy rule-based system applied to real-world Big Data - the GEO-CRADLE and LUCAS soil spectral libraries" @default.
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• W2897567604 doi "https://doi.org/10.1109/fuzz-ieee.2018.8491489" @default.
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