An efficient genetic selection of the presentation order in simplified fuzzy ARTMAP patterns

Jeonghyun Baek, Heesung Lee, Byungyun Lee, Heejin Lee, Euntai Kim

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Simplified fuzzy ARTMAP (SFAM) is used in numerous classification problems due to its high discriminant power and low training time. However, the performance of SFAM is affected by the presentation order of the training patterns. The genetic algorithm (GA) can be considered as a solution to the problem because the selection of the training pattern order is a complicated combinatorial problem in a large search space. In this paper, a new genetic ordering method for SFAM is proposed to improve the performance of the algorithm. Special genetic operators are employed in the genetic evolution. Compared to the conventional methods, the proposed SFAM demonstrates better classification performance since it can efficiently deliver the desirable properties of parents to their offspring. To demonstrate the performance of the proposed method, we perform experiments on various databases from the UCI repository.

Original languageEnglish
Pages (from-to)101-107
Number of pages7
JournalApplied Soft Computing Journal
Volume22
DOIs
Publication statusPublished - 2014 Sep

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology ( NRF-2013R1A2A2A01015624 ).

All Science Journal Classification (ASJC) codes

  • Software

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