On the use of MapReduce to build linguistic fuzzy rule based classification systems for big data

Big data has become one of the emergent topics when learning from data is involved. The notorious increment in the data generation has directed the attention towards the obtaining of effective models that are able to analyze and extract knowledge from these colossal data sources. However, the vast amount of data, the variety of the sources and the need for an immediate intelligent response pose a critical challenge to traditional learning algorithms. To be able to deal with big data, we propose the usage of a linguistic fuzzy rule based classification system, which we have called Chi-FRBCS-BigData. As a fuzzy method, it is able deal with the uncertainty that is inherent to the variety and veracity of big data and because of the usage of linguistic fuzzy rules it is able to provide an interpretable and effective classification model. This method is based on the MapReduce framework, one of the most popular approaches for big data nowadays, and has been developed in two different versions: Chi-FRBCS-BigData-Max and Chi-FRBCS-BigData-Ave. The good performance of the Chi-FRBCS-BigData approach is supported by means of an experimental study over six big data problems. The results show that the proposal is able to provide competitive results, obtaining more precise but slower models in the Chi-FRBCS-BigData-Ave alternative and faster but less accurate classification results for Chi-FRBCS-BigData-Max.