Fuzzy Min-Max Neural Network (FMNN) Classifier has acquired significance owing to its unique properties of single-pass training, non-linear classification, and adaptability for incremental learning. Since its inception in 1992, FMNN has witnessed several extensions, modifications, and utilization in various applications. But very few works are done in the literature for enhancing the scalability of FMNN. In recent years, MapReduce framework is used extensively for scaling machine learning algorithms. The existing MapReduce approach for FMNN (MRFMNN) is found to be having limitations in load balancing and in achieving good generalizability. This work proposes MRCFMNN Algorithm for overcoming these limitations. MRCFMNN induces an ensemble of centroid-based FMNN Classifiers for achieving higher generalizability with load balancing. Four ensemble strategies are proposed for combining the individual classifier results. The comparative experimental results using benchmark large decision systems were conducted on Apache Spark MapReduce cluster. The results empirically establish the relevance of the proposed MRCFMNN by achieving significantly better classification accuracy in most of the datasets over MRFMNN.