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

The main drawback of Fuzzy Rule-Based Classification Systems (FRBCSs) when they are applied in Big Data problems is the lack of scalability. Previously proposed approaches consist in concurrently fitting several Chi et al. FRBCSs whose rule bases are then aggregated to obtain the final model. This methodology is seriously affected by the degree of parallelism used for the execution of the algorithm, showing a significant decrease in classification performance as the degree of parallelism increases. This work focuses on the design of a new FRBCS for Big Data classification problems (CHI-BD) that generates exactly the same rule base regardless of the degree of parallelism. Our approach recovers the model that would be built by the original Chi et al. algorithm if it was able to deal with Big Data problems.