Abstract
Fuzzy Min-Max Neural Network (FMNN) is a pattern classification algorithm which incorporates fuzzy sets and neural network. It is most suitable for online algorithms. Based on this, a MapReduce-based Fuzzy Min-Max Neural Network (MRFMNN) algorithm for pattern classification is proposed using Twister framework. MapReduce approach is used for scaling up the FMNN for massive large scale datasets. We used standard membership, expansion and the contraction functions of the traditional FMNN algorithm. The performance of the MRFMNN is tested by using several benchmark and synthetic datasets against the traditional FMNN. Results empirically established that MRFMNN achieves significant computational gains over FMNN without compromising classification accuracy.
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