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The impact of scaling on Support Vector Machine in Breast Cancer Diagnosis

by Elsayed Badr, Mustafa Abdulsalam, Hagar Ahmed
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 19
Year of Publication: 2020
Authors: Elsayed Badr, Mustafa Abdulsalam, Hagar Ahmed
10.5120/ijca2020920710

Elsayed Badr, Mustafa Abdulsalam, Hagar Ahmed . The impact of scaling on Support Vector Machine in Breast Cancer Diagnosis. International Journal of Computer Applications. 175, 19 ( Sep 2020), 15-19. DOI=10.5120/ijca2020920710

@article{ 10.5120/ijca2020920710,
author = { Elsayed Badr, Mustafa Abdulsalam, Hagar Ahmed },
title = { The impact of scaling on Support Vector Machine in Breast Cancer Diagnosis },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2020 },
volume = { 175 },
number = { 19 },
month = { Sep },
year = { 2020 },
issn = { 0975-8887 },
pages = { 15-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number19/31559-2020920710/ },
doi = { 10.5120/ijca2020920710 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:25:28.486428+05:30
%A Elsayed Badr
%A Mustafa Abdulsalam
%A Hagar Ahmed
%T The impact of scaling on Support Vector Machine in Breast Cancer Diagnosis
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 19
%P 15-19
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

By using support vector machine (SVM) and the grid technique Badr et al. [1] introduced new scaling techniques on the data set Wisconsin from UCI machine learning with a total 569 rows and 33 columns. These scaling techniques overcame the standard normalization techniques. In this paper, three new scaling techniques are proposed by using SVM and the grid technique on the the data set Wisconsin from UCI machine learning with a total 569 rows and 32 columns. These scaling techniques are: (i) de Buchet for p = ( ∞) (ii) Lp-norm for p = (∞) (iii) Entropy . Experimental results show that SVM with new scaling techniques achieves 98.60 % , 98.42 % and 98.42 % accuracy against to the standard normalization by 96.49 %.

References
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  33. E. M. Badr and Sultan Almotiari (2019) " On a Dual Direct Cosine Simplex Type Algorithm and Its Computational Behavior" Mathematical Problems in Engineering Volume 2020, Article ID 7361092, 8 pages. https://doi.org/10.1155/2020/7361092.
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Index Terms

Computer Science
Information Sciences

Keywords

Machine Learning Breast Cancer Support vector machine scaling techniques.

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