A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules Río García, Sara del López, Victoria Benítez Sánchez, José Manuel Herrera Triguero, Francisco Fuzzy rule based classification systems Big data MapReduce Hadoop Rules fusion The big data term is used to describe the exponential data growth that has recently occurred and represents an immense challenge for traditional learning techniques. To deal with big data classification problems we propose the Chi-FRBCS-BigData algorithm, a linguistic fuzzy rule-based classification system that uses the MapReduce framework to learn and fuse rule bases. It has been developed in two versions with different fusion processes. An experimental study is carried out and the results obtained show that the proposal is able to handle these problems providing competitive results 2020-10-27T12:00:11Z 2020-10-27T12:00:11Z 2015-05-04 info:eu-repo/semantics/article del Rio, S., Lopez, V., Benítez, J. M., & Herrera, F. (2015). A mapreduce approach to address big data classification problems based on the fusion of linguistic fuzzy rules. International Journal of Computational Intelligence Systems, 8(3), 422-437. [doi:10.1080/18756891.2015.1017377] http://hdl.handle.net/10481/63912 10.1080/18756891.2015.1017377 eng http://creativecommons.org/licenses/by-nc/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial 3.0 España ATLANTIS PRESS