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Journal of Artificial Intelligence and Soft Computing Research

The Journal of Polish Neural Network Society, the University of Social Sciences in Lodz & Czestochowa University of Technology

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2083-2567
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Self-Configuring Hybrid Evolutionary Algorithm for Fuzzy Imbalanced Classification with Adaptive Instance Selection

Vladimir Stanovov
  • Corresponding author
  • Institute of Computer Science and Telecommunications, Siberian State Aerospace University Krasnoyarskii rabochii ave. 31, 660014, Krasnoyarsk, Russian Federation
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Eugene Semenkin
  • Institute of Computer Science and Telecommunications, Siberian State Aerospace University Krasnoyarskii rabochii ave. 31, 660014, Krasnoyarsk, Russian Federation
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Olga Semenkina
  • Institute of Computer Science and Telecommunications, Siberian State Aerospace University Krasnoyarskii rabochii ave. 31, 660014, Krasnoyarsk, Russian Federation
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2016-06-10 | DOI: https://doi.org/10.1515/jaiscr-2016-0013

Abstract

A novel approach for instance selection in classification problems is presented. This adaptive instance selection is designed to simultaneously decrease the amount of computation resources required and increase the classification quality achieved. The approach generates new training samples during the evolutionary process and changes the training set for the algorithm. The instance selection is guided by means of changing probabilities, so that the algorithm concentrates on problematic examples which are difficult to classify. The hybrid fuzzy classification algorithm with a self-configuration procedure is used as a problem solver. The classification quality is tested upon 9 problem data sets from the KEEL repository. A special balancing strategy is used in the instance selection approach to improve the classification quality on imbalanced datasets. The results prove the usefulness of the proposed approach as compared with other classification methods.

Keywords: Fuzzy classification; instance selection; genetic fuzzy system; self-configuration

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About the article

Published Online: 2016-06-10

Published in Print: 2016-07-01


Citation Information: Journal of Artificial Intelligence and Soft Computing Research, Volume 6, Issue 3, Pages 173–188, ISSN (Online) 2083-2567, DOI: https://doi.org/10.1515/jaiscr-2016-0013.

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© 2016. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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