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Fuzzy rule based classification systems for big data with MapReduce: granularity analysis

Author

Listed:
  • Alberto Fernández

    (University of Granada)

  • Sara Río

    (University of Granada)

  • Abdullah Bawakid

    (King Abdulaziz University (KAU))

  • Francisco Herrera

    (University of Granada
    King Abdulaziz University (KAU))

Abstract

Due to the vast amount of information available nowadays, and the advantages related to the processing of this data, the topics of big data and data science have acquired a great importance in the current research. Big data applications are mainly about scalability, which can be achieved via the MapReduce programming model.It is designed to divide the data into several chunks or groups that are processed in parallel, and whose result is “assembled” to provide a single solution. Among different classification paradigms adapted to this new framework, fuzzy rule based classification systems have shown interesting results with a MapReduce approach for big data. It is well known that the performance of these types of systems has a strong dependence on the selection of a good granularity level for the Data Base. However, in the context of MapReduce this parameter is even harder to determine as it can be also related with the number of Maps chosen for the processing stage. In this paper, we aim at analyzing the interrelation between the number of labels of the fuzzy variables and the scarcity of the data due to the data sampling in MapReduce. Specifically, we consider that as the partitioning of the initial instance set grows, the level of granularity necessary to achieve a good performance also becomes higher. The experimental results, carried out for several Big Data problems, and using the Chi-FRBCS-BigData algorithms, support our claims.

Suggested Citation

  • Alberto Fernández & Sara Río & Abdullah Bawakid & Francisco Herrera, 2017. "Fuzzy rule based classification systems for big data with MapReduce: granularity analysis," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 11(4), pages 711-730, December.
    • Handle: RePEc:spr:advdac:v:11:y:2017:i:4:d:10.1007_s11634-016-0260-z
    DOI: 10.1007/s11634-016-0260-z
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    References listed on IDEAS

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    1. Vivien Marx, 2013. "The big challenges of big data," Nature, Nature, vol. 498(7453), pages 255-260, June.
    2. Chris A. Mattmann, 2013. "A vision for data science," Nature, Nature, vol. 493(7433), pages 473-475, January.
    3. Caf, . "Programa de bosques," Books, CAF Development Bank Of Latinamerica, number 533.
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