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CHI-BD: A fuzzy rule-based classification system for Big Data classification problems
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DOI：10.1016/j.fss.2017.07.003.png)
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Abstract
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The previous Fuzzy Rule-Based Classification Systems (FRBCSs) for Big Data problems consist in concurrently learning multiple Chi et al. FRBCSs whose rule bases are then aggregated. The problem of this approach is that different models are obtained when varying the configuration of the cluster, becoming less accurate as more computing nodes are added. Our aim with this work is to design a new FRBCS for Big Data classification problems (CHI-BD) which is able to provide exactly the same model as the one that would be obtained by the original Chi et al. algorithm if it could be executed with this quantity of data. In order to do so, we take advantage of the suitability of the Chi et al. algorithm for the MapReduce paradigm, solving the problems of the previous approach, which lead us to obtain the same model (i.e., classification accuracy) regardless of the number of computing nodes considered. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Fuzzy Rule-Based Classification Systems
Big Data
Hadoop
MapReduce
Imbalanced datasets
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