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MR-DIS: democratic instance selection for big data by MapReduce

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Abstract

Instance selection is a popular preprocessing task in knowledge discovery and data mining. Its purpose is to reduce the size of data sets maintaining their predictive capabilities. The usual emerging problem at this point is that these methods quite often suffer of high computational complexity, which becomes highly inconvenient for processing huge data sets. In this paper, a parallel implementation for the instance selection algorithm Democratic Instance Selection (DIS) is presented. The main advantages of the DIS algorithm turn out to be its computational complexity, linear in the number of instances, as well as its internal structure, intuitively parallelizable. The purpose of this paper is threefold: firstly, the design of the DIS algorithm by following the MapReduce model; secondly, its implementation in the popular big data framework Spark; and finally, its empirical comparison over large-scale data sets. The results show that the processing time is reduced in a linear manner as the number of Spark executors increases, what makes it suitable for big data applications. In addition, the algorithm is publicly accessible to the scientific community.

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Notes

  1. The subset selected by the algorithm is indistinctly referred to as filtered or selected set in the present paper.

  2. We recommend the work of S. García et al. [10] for readers interested in this field.

  3. In Spark, the process located between the map and the reduce phases is usually referred to as shuffle.

  4. Author: Alejandro González-Rogel, https://bitbucket.org/agr00095/tfg-alg.-seleccion-instancias-spark.

  5. In the Spark framework, each worker node has one or more executors, each one of which completes a task. A processor was assigned to each executor in the experimental work.

  6. In [12] the percentage of instances used for error estimation in massive data sets was \(0.1\%\), but the use of a parallel implementation of 1-NN permits an increase in this percentage, improving the precision of the estimation.

  7. Google Cloud Platform: https://cloud.google.com/dataproc/.

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Acknowledgements

This work was funded by the Ministry of Economy and Competitiveness, Project TIN 2015-67534-P.

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Authors and Affiliations

  1. University of Burgos, Avda. Cantabria s/n, 09006, Burgos, Burgos, Spain

    Álvar Arnaiz-González, Alejandro González-Rogel, José-Francisco Díez-Pastor & Carlos López-Nozal

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  1. Álvar Arnaiz-González
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  2. Alejandro González-Rogel
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  3. José-Francisco Díez-Pastor
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  4. Carlos López-Nozal
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Correspondence to Álvar Arnaiz-González.

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Arnaiz-González, Á., González-Rogel, A., Díez-Pastor, JF. et al. MR-DIS: democratic instance selection for big data by MapReduce. Prog Artif Intell 6, 211–219 (2017). https://doi.org/10.1007/s13748-017-0117-5

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