A genetic programming classifier design approach for cell images

Aydin Akyol*, Yusuf Yaslan, Osman Kaan Erol

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

This paper describes an approach for the use of genetic programming (GP) in classification problems and it is evaluated on the automatic classification problem of pollen cell images. In this work, a new reproduction scheme and a new fitness evaluation scheme are proposed as advanced techniques for GP classification applications. Also an effective set of pollen cell image features is defined for cell images. Experiments were performed on Bangor/Aberystwyth Pollen Image Database and the algorithm is evaluated on challenging test configurations. We reached at 96 % success rate on the average together with significant improvement in the speed of convergence.

Original languageEnglish
Title of host publicationSymbolic and Quantitative Approaches to Reasoning with Uncertainty - 9th European Conference, ECSQARU 2007, Proceedings
PublisherSpringer Verlag
Pages878-888
Number of pages11
ISBN (Print)9783540752554
DOIs
Publication statusPublished - 2007
Event9th European Conference on Symbolic and Qualitative Approaches to Reasoning with Uncertainty, ECSQARU 2007 - Hammamet, Tunisia
Duration: 31 Oct 20072 Nov 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4724 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th European Conference on Symbolic and Qualitative Approaches to Reasoning with Uncertainty, ECSQARU 2007
Country/TerritoryTunisia
CityHammamet
Period31/10/072/11/07

Keywords

  • Cell classification
  • Classifier design
  • Genetic programming
  • Pollen classification

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