Developing an appropriate data normalization method

Balemir Uragun, Ramesh Rajan

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

3 Citations (Scopus)

Abstract

The issue of data normalization has been extensively studied with respect to many different applications, for example it has especially became an essential topic in study of gene expression. However, before implementing any normalization of data, it is first necessary to establish whether the data requires to be normalized, and this can be determined by a simple test-bench test. Our test-bench compared seven well-known normalization techniques for nine possible Interaural Level Differences (ILD) sensitivity functions, types of biological data. These nine realistic ILD functions were systematically modified to be delivered at the most suitable normalization technique. This method then helped to select a coherent normalization technique for the data before applying the statistical technique of Cluster Analysis which can be explored in future studies. 

Original languageEnglish
Title of host publicationProceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011
EditorsXue-wen Chen, Tharam Dillon, Hisao Ishbuchi, Jian Pei, Haixun Wang, M. Arif Wani
Pages195-199
Number of pages5
Volume2
DOIs
Publication statusPublished - 2011
EventInternational Conference on Machine Learning and Applications 2011 - Honolulu, United States of America
Duration: 18 Dec 201121 Dec 2011
Conference number: 10th
http://www.icmla-conference.org/icmla11/

Conference

ConferenceInternational Conference on Machine Learning and Applications 2011
Abbreviated titleICMLA 2011
CountryUnited States of America
CityHonolulu
Period18/12/1121/12/11
Internet address

Keywords

  • Data Normalization
  • Interaural Level Difference

Cite this

Uragun, B., & Rajan, R. (2011). Developing an appropriate data normalization method. In X. Chen, T. Dillon, H. Ishbuchi, J. Pei, H. Wang, & M. A. Wani (Eds.), Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011 (Vol. 2, pp. 195-199). [6147672] https://doi.org/10.1109/ICMLA.2011.53
Uragun, Balemir ; Rajan, Ramesh. / Developing an appropriate data normalization method. Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011. editor / Xue-wen Chen ; Tharam Dillon ; Hisao Ishbuchi ; Jian Pei ; Haixun Wang ; M. Arif Wani. Vol. 2 2011. pp. 195-199
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Uragun, B & Rajan, R 2011, Developing an appropriate data normalization method. in X Chen, T Dillon, H Ishbuchi, J Pei, H Wang & MA Wani (eds), Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011. vol. 2, 6147672, pp. 195-199, International Conference on Machine Learning and Applications 2011, Honolulu, United States of America, 18/12/11. https://doi.org/10.1109/ICMLA.2011.53

Developing an appropriate data normalization method. / Uragun, Balemir; Rajan, Ramesh.

Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011. ed. / Xue-wen Chen; Tharam Dillon; Hisao Ishbuchi; Jian Pei; Haixun Wang; M. Arif Wani. Vol. 2 2011. p. 195-199 6147672.

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

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AB - The issue of data normalization has been extensively studied with respect to many different applications, for example it has especially became an essential topic in study of gene expression. However, before implementing any normalization of data, it is first necessary to establish whether the data requires to be normalized, and this can be determined by a simple test-bench test. Our test-bench compared seven well-known normalization techniques for nine possible Interaural Level Differences (ILD) sensitivity functions, types of biological data. These nine realistic ILD functions were systematically modified to be delivered at the most suitable normalization technique. This method then helped to select a coherent normalization technique for the data before applying the statistical technique of Cluster Analysis which can be explored in future studies. 

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Uragun B, Rajan R. Developing an appropriate data normalization method. In Chen X, Dillon T, Ishbuchi H, Pei J, Wang H, Wani MA, editors, Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011. Vol. 2. 2011. p. 195-199. 6147672 https://doi.org/10.1109/ICMLA.2011.53