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Adaptive artificial datasets to discover the effects of domain features for classification tasks

Published: 06 July 2013 Publication History

Abstract

This paper described an automated pattern generator to generate various synthetic data sets for classification problems, where the problem's complexity can be manipulated autonomously. The Tabu Search technique has been applied in the pattern generator to discover the best combination of domain features in order to adjust the complexity levels of the problem. Experiments confirm that the pattern generator was able to tune the problem's complexity so that it can either increase or decrease the classification performance. The novel contributions in this work enable the effect of domain features that alter classification performance, to become human readable. This work provides a new method for generating artificial datasets at various levels of difficulty where the difficulty levels can be tuned autonomously.

References

[1]
S. Marzukhi, W. N. Browne, and M. Zhang. Two-cornered learning classifier systems for pattern generation and classification. In The 12th Genetic and Evolutionary Computation Conference (GECCO 2012), pages 895--902. ACM, 2012.
[2]
S. Wilson. Coevolution of Pattern Generators and Recognizers. Lecture Notes in Computer Science (LNCS), Volume 6471/2010(1):38--46, 2010.

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  1. Adaptive artificial datasets to discover the effects of domain features for classification tasks

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      cover image ACM Conferences
      GECCO '13 Companion: Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
      July 2013
      1798 pages
      ISBN:9781450319645
      DOI:10.1145/2464576
      • Editor:
      • Christian Blum,
      • General Chair:
      • Enrique Alba
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      New York, NY, United States

      Publication History

      Published: 06 July 2013

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      Author Tags

      1. learning classifier systems
      2. pattern classification

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      GECCO '13
      Sponsor:
      GECCO '13: Genetic and Evolutionary Computation Conference
      July 6 - 10, 2013
      Amsterdam, The Netherlands

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      Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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