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The previous Fuzzy Rule-Based Classification Systems (FRBCSs) for Big Data problems consist in concurrently learning multiple Chi et al. FRBCSs whose rule bases are then aggregated. The problem of this approach is that different models are obtained when varying the configuration of the cluster, becoming less accurate as more computing nodes are added. Our aim with this work is to design a new FRBCS...
In this work we present an optimization of the only Fuzzy Rule-Based Classification System that is able to face Big Data classification problems to date, i.e., Chi-FRBCS-BigDataCS. The aim of this optimization is to speed up the learning process of the algorithm without affecting the model obtained. Our proposal is based on the usage of Look-Up-Tables to pre-compute the membership degrees of the different...
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