A global distributed approach to the Chi et al. fuzzy rule-based classification system for big data classification problems

2017 ◽  
Author(s):  
Mikel Elkano ◽  
Mikel Galar ◽  
Jose Sanz ◽  
Gracaliz P. Dimuro ◽  
Humberto Bustince
Keyword(s):  
Big Data ◽  
Classification System ◽  
Fuzzy Rule ◽  
Data Classification ◽  
Classification Problems ◽  
Rule Based ◽  
Distributed Approach ◽  
Big Data Classification

CHI-BD: A fuzzy rule-based classification system for Big Data classification problems

2018 ◽  
Vol 348 ◽  
pp. 75-101 ◽  
Author(s):  
Mikel Elkano ◽  
Mikel Galar ◽  
Jose Sanz ◽  
Humberto Bustince
Keyword(s):  
Big Data ◽  
Classification System ◽  
Fuzzy Rule ◽  
Data Classification ◽  
Classification Problems ◽  
Rule Based ◽  
Big Data Classification

scholarly journals CFM-BD: A Distributed Rule Induction Algorithm for Building Compact Fuzzy Models in Big Data Classification Problems

2020 ◽  
Vol 28 (1) ◽  
pp. 163-177 ◽  
Author(s):  
Mikel Elkano ◽  
Jose Antonio Sanz ◽  
Edurne Barrenechea ◽  
Humberto Bustince ◽  
Mikel Galar
Keyword(s):  
Big Data ◽  
Data Classification ◽  
Rule Induction ◽  
Classification Problems ◽  
Fuzzy Models ◽  
Big Data Classification

A New MapReduce Approach with Dynamic Fuzzy Inference for Big Data Classification Problems

2018 ◽  
Vol 12 (3) ◽  
pp. 40-54 ◽  
Author(s):  
Shangzhu Jin ◽  
Jun Peng ◽  
Dong Xie
Keyword(s):  
Big Data ◽  
Fuzzy Inference ◽  
Interpolation Method ◽  
Fuzzy Rule ◽  
Classification Problems ◽  
Fuzzy Interpolation ◽  
Big Data Applications ◽  
Data Environment ◽  
Big Data Classification ◽  
Reasoning Technique

Currently, big data and its applications have become one of the emergent topics. In practice, MapReduce framework and its different extensions are the most popular approaches for big data. Fuzzy system based models stand out for many applications. However, when a given observation has no overlap with antecedent values, no rule can be invoked in classical fuzzy inference can also appear in big data environment, and therefore no consequence can be derived. Fortunately, fuzzy rule interpolation techniques can support inference in such cases. Combining traditional fuzzy reasoning technique and fuzzy interpolation method may promote the accuracy of inference conclusion. Therefore, in this article, an initial investigation into the framework of MapReduce with dynamic fuzzy inference/interpolation for big data applications (BigData-DFRI) is reported. The results of an experimental investigation of this method are represented, demonstrating the potential and efficacy of the proposed approach.

A distributed approach to multi-objective evolutionary generation of fuzzy rule-based classifiers from big data

2017 ◽  
Vol 415-416 ◽  
pp. 319-340 ◽  
Author(s):  
Andrea Ferranti ◽  
Francesco Marcelloni ◽  
Armando Segatori ◽  
Michela Antonelli ◽  
Pietro Ducange
Keyword(s):  
Big Data ◽  
Fuzzy Rule ◽  
Rule Based ◽  
Multi Objective ◽  
Distributed Approach
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