CHI-BD: A fuzzy rule-based classification system for Big Data classification problems

Currently, big data and its applications have become one of the emergent topics. In practice, MapReduce framework and its different extensions are the most popular approaches for big data. Fuzzy system based models stand out for many applications. However, when a given observation has no overlap with antecedent values, no rule can be invoked in classical fuzzy inference can also appear in big data environment, and therefore no consequence can be derived. Fortunately, fuzzy rule interpolation techniques can support inference in such cases. Combining traditional fuzzy reasoning technique and fuzzy interpolation method may promote the accuracy of inference conclusion. Therefore, in this article, an initial investigation into the framework of MapReduce with dynamic fuzzy inference/interpolation for big data applications (BigData-DFRI) is reported. The results of an experimental investigation of this method are represented, demonstrating the potential and efficacy of the proposed approach.

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A MapReduce Approach to Address Big Data Classification Problems Based on the Fusion of Linguistic Fuzzy Rules

International Journal of Computational Intelligence Systems ◽

10.1080/18756891.2015.1017377 ◽

2015 ◽

Vol 8 (3) ◽

pp. 422-437 ◽

Cited By ~ 61

Author(s):

Sara del Río ◽

Victoria López ◽

José Manuel Benítez ◽

Francisco Herrera

Keyword(s):

Big Data ◽

Data Classification ◽

Fuzzy Rules ◽

Classification Problems ◽

Big Data Classification ◽

Linguistic Fuzzy Rules

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A Novel Approach With Dynamic Fuzzy Inference for Big Data Classification Problems

Advances in Computational Intelligence and Robotics - Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence ◽

10.4018/978-1-7998-3038-2.ch003 ◽

2020 ◽

pp. 43-58

Author(s):

Shangzhu Jin ◽

Jun Peng

Keyword(s):

Big Data ◽

Missing Values ◽

Fuzzy Inference ◽

Interpolation Method ◽

Fuzzy Rule ◽

Data Sets ◽

Classification Problems ◽

Fuzzy Interpolation ◽

Novel Approach ◽

Big Data Classification

Currently, big data and its applications have become emergent topics. To deal with the uncertainty in data sets, fuzzy system-based models were explored and stand out for many applications. However, when a given observation has no overlap with antecedent values, no rule can be invoked, or even the invoked rules with missing values in classical fuzzy inference can also appear in big data environment, and therefore, no consequence can be derived. Fortunately, fuzzy rule interpolation techniques can support inference in such cases. Combining traditional fuzzy reasoning technique and fuzzy interpolation method may promote the accuracy of inference conclusion. Therefore, in this chapter, an initial investigation into the framework of MapReduce with dynamic fuzzy inference/interpolation for big data applications (BigData-DFRI) is reported. The results of an experimental investigation of this method are represented, demonstrating the potential and efficacy of the proposed approach.

Download Full-text