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On Granular Rough Computing: Covering by Joint and Disjoint Granules in Epsilon Concept Dependent Granulation

  • Piotr Artiemjew
  • Jacek Szypulski
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 57)

Abstract

In this work we present the optimization methods of epsilon concept-dependent granulation. We consider two cases of parallel covering and granulation, based on joint and disjoint granules. Additionally we check two variants of majority voting, the first one based on descriptors, which are epsilon-indiscernible with the centers of granules, and the second variant uses all descriptors of respective granules. We verify the effectiveness of our methods on the real data sets from UCI Repository using the SVM classifier. It turned out that disjoint granules versus joint give almost identical results of classification with a significant acceleration of the granulation process. Additionally, the majority voting, based on the epsilon indiscernible descriptors, stabilised the process of granulation in terms of the accuracy of classification. This is a significant result, which lets us to accelerate the process of classification for many popular classifiers at least for k-NN, Naive Bayes, many rough set methods and the SVM classifier, which is supported by our recent works.

Keywords

Rough sets Decision systems SVM Granular rough computing Epsilon concept-dependent granulation Majority voting 

Notes

Acknowledgments

The research has been supported by grant 1309-802 from the Ministry of Science and Higher Education of the Republic of Poland.

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© Springer International Publishing Switzerland 2016

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Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceUniversity of Warmia and MazuryOlsztynPoland

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