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A Genetic Programming Classifier Design Approach for Cell Images

by: Aydin Akyol, Yusuf Yaslan, and Osman Kaan Erol

In: Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU, Vol. 4724 Hammamet, Tunisia: Springer (October 2007) , p. 878--888.

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 96percent success rate on the average together with significant improvement in the speed of convergence.

BibTeX record

@inproceedings{conf/ecsqaru/AkyolYE07,
  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 96percent success
                 rate on the average together with significant
                 improvement in the speed of convergence.},
  added-at = {2008-06-19T17:35:00.000+0200},
  address = {Hammamet, Tunisia},
  author = {Akyol, Aydin and Yaslan, Yusuf and Erol, Osman Kaan},
  bibdate = {2007-09-17},
  bibsource = {DBLP,
                 http://dblp.uni-trier.de/db/conf/ecsqaru/ecsqaru2007.html#AkyolYE07},
  biburl = {http://www.bibsonomy.org/bibtex/21291500ad88bbbdea4768411424f1a84/brazovayeye},
  booktitle = {Proceedings of the 9th European Conference on Symbolic
                 and Quantitative Approaches to Reasoning with
                 Uncertainty, ECSQARU},
  doi = {doi:10.1007/978-3-540-75256-1_76},
  editor = {Mellouli, Khaled},
  interhash = {4b75d99b61f9dce882099da42f54dcb2},
  intrahash = {1291500ad88bbbdea4768411424f1a84},
  isbn13 = {978-3-540-75255-4},
  keywords = {algorithms, cell classification classification, classifier design, genetic pollen programming,},
  month = {October 31 - November 2},
  notes = {cf email GP mailing list Mon, 24 Dec 2007 13:42:59
                 +0200

You may find MATLAB codes from my Web Page below:
                 www3.itu.edu.tr/~okerol

you may refer to: O.K. Erol, I.Eksin, 2006. A New
                 Optimization Method: Big-Bang Big-Crunch, Advences in
                 Engineering Software, Elsevier, Vol 37, No.2, 106-111
                 doi:10.1016/j.advengsoft.2005.04.005},
  pages = {878--888},
  publisher = {Springer},
  series = {Lecture Notes in Computer Science},
  timestamp = {2008-06-19T17:35:00.000+0200},
  title = {A Genetic Programming Classifier Design Approach for
                 Cell Images},
  volume = 4724,
  year = 2007
}

Endnote record

%0 Conference Paper
%1 conf/ecsqaru/AkyolYE07
%A Akyol, Aydin
%A Yaslan, Yusuf
%A Erol, Osman Kaan
%B Proceedings of the 9th European Conference on Symbolic
                 and Quantitative Approaches to Reasoning with
                 Uncertainty, ECSQARU
%C Hammamet, Tunisia
%D 2007
%E Mellouli, Khaled
%I Springer
%K 
%P 878--888
%T A Genetic Programming Classifier Design Approach for
                 Cell Images
%R doi:10.1007/978-3-540-75256-1_76
%V 4724
%X 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 96percent success
                 rate on the average together with significant
                 improvement in the speed of convergence.