GPGPU Implementation of Fuzzy Rule-Based Classifiers

  • Tomoharu Nakashima
  • Keigo Tanaka
  • Noriyuki Fujimoto
  • Ryosuke Saga
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 16)

Abstract

This paper presents a parallel implementation of fuzzy-rule-based classifiers using a GPGPU (General Purpose Graphics Processing Unit). There are two steps in the process of fuzzy rule-based classification: Fuzzy-rule generation from training data and classification of an unseen input pattern. The proposed implementation parallelizes these two steps. In the step of fuzzy-rule generation from training patterns, the membership calculation of a training pattern for available fuzzy sets is simultaneously processed. On the other hand, the membership calculation of an unseen pattern for the generated fuzzy if-then rules is simultaneously processed in the step of the classification of the pattern. The efficiency of the parallelization is evaluated through a series of computational experiments. The effect of the parallelization is evaluated for each step of fuzzy rule-based classifiers. The results of the computational experiments show that the proposed implementation successfully improve the speed of fuzzy rule-based classifiers.

Keywords

Memory Access Training Pattern Fuzzy Partition Antecedent Part General Purpose Graphic Processing Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tomoharu Nakashima
    • 1
  • Keigo Tanaka
    • 1
  • Noriyuki Fujimoto
    • 1
  • Ryosuke Saga
    • 1
  1. 1.Osaka Prefecture UniversitySakaiJapan

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