Genetic algorithms for wavenumber selection in forensic differentiation of paper by linear discriminant analysis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Selection of the most significant variables, i.e. the wavenumber, from an infrared (IR) spectrum is always difficult to be achieved. In this preliminary paper, the feasibility of genetic algorithms (GA) in identifying most informative wavenumbers from 150 IR spectra of papers was investigated. The list of selected wavenumbers was then employed in Linear Discriminant Analysis (LDA). GA procedure was repeated 30 times to get different lists of variables. Then the performances of LDA models were estimated via leave-one-out cross-validation. A total of six to eight wavenumbers were identified to be valuable variables in the GA procedures. All the 30 LDA models achieve correct classification rates between 97.3% to 100.0%. Therefore the GA-LDA model could be a suitable tool for differentiating white papers that appeared to be highly similar in their IR fingerprints.

Original languageEnglish
Title of host publicationAdvances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015
PublisherAmerican Institute of Physics Inc.
Volume1750
ISBN (Electronic)9780735414075
DOIs
Publication statusPublished - 21 Jun 2016
Event23rd Malaysian National Symposium of Mathematical Sciences: Advances in Industrial and Applied Mathematics, SKSM 2015 - Johor Bahru, Malaysia
Duration: 24 Nov 201526 Nov 2015

Other

Other23rd Malaysian National Symposium of Mathematical Sciences: Advances in Industrial and Applied Mathematics, SKSM 2015
CountryMalaysia
CityJohor Bahru
Period24/11/1526/11/15

Fingerprint

genetic algorithms
lists
infrared spectra

Keywords

  • classification
  • forensic science
  • Genetic algorithms
  • IR spectrum
  • linear discriminant analysis

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Liong, C. Y., Lee, L. C., Osman, K., & Jemain, A. A. (2016). Genetic algorithms for wavenumber selection in forensic differentiation of paper by linear discriminant analysis. In Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015 (Vol. 1750). [060017] American Institute of Physics Inc.. https://doi.org/10.1063/1.4954622

Genetic algorithms for wavenumber selection in forensic differentiation of paper by linear discriminant analysis. / Liong, Choong Yeun; Lee, Loong Chuen; Osman, Khairul; Jemain, Abdul Aziz.

Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015. Vol. 1750 American Institute of Physics Inc., 2016. 060017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liong, CY, Lee, LC, Osman, K & Jemain, AA 2016, Genetic algorithms for wavenumber selection in forensic differentiation of paper by linear discriminant analysis. in Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015. vol. 1750, 060017, American Institute of Physics Inc., 23rd Malaysian National Symposium of Mathematical Sciences: Advances in Industrial and Applied Mathematics, SKSM 2015, Johor Bahru, Malaysia, 24/11/15. https://doi.org/10.1063/1.4954622
Liong CY, Lee LC, Osman K, Jemain AA. Genetic algorithms for wavenumber selection in forensic differentiation of paper by linear discriminant analysis. In Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015. Vol. 1750. American Institute of Physics Inc. 2016. 060017 https://doi.org/10.1063/1.4954622
Liong, Choong Yeun ; Lee, Loong Chuen ; Osman, Khairul ; Jemain, Abdul Aziz. / Genetic algorithms for wavenumber selection in forensic differentiation of paper by linear discriminant analysis. Advances in Industrial and Applied Mathematics: Proceedings of 23rd Malaysian National Symposium of Mathematical Sciences, SKSM 2015. Vol. 1750 American Institute of Physics Inc., 2016.
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abstract = "Selection of the most significant variables, i.e. the wavenumber, from an infrared (IR) spectrum is always difficult to be achieved. In this preliminary paper, the feasibility of genetic algorithms (GA) in identifying most informative wavenumbers from 150 IR spectra of papers was investigated. The list of selected wavenumbers was then employed in Linear Discriminant Analysis (LDA). GA procedure was repeated 30 times to get different lists of variables. Then the performances of LDA models were estimated via leave-one-out cross-validation. A total of six to eight wavenumbers were identified to be valuable variables in the GA procedures. All the 30 LDA models achieve correct classification rates between 97.3{\%} to 100.0{\%}. Therefore the GA-LDA model could be a suitable tool for differentiating white papers that appeared to be highly similar in their IR fingerprints.",
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