Standardized Variable Distances (SVD)

Version 1.0 (30 KB) by Abdullah Elen

The SVD is a novel machine learning algorithm for multiclass classification.

https://www.researchgate.net/project/Elens-Machine-Learning

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Updated 20 Dec 2020

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In this study, a novel machine learning algorithm for multiclass classification is presented. The proposed method is designed based on the Minimum Distance Classifier (MDC) algorithm. The MDC is variance-insensitive because it classifies input vectors by calculating their distances/similarities with respect to class-centroids (average value of input vectors of a class). As it is known, real-world data contains certain proportions of noise. This situation negatively affects the performance of the MDC. To overcome this problem, we developed a variance-sensitive model, which we call Standardized Variable Distances (SVD), considering the standard deviation and z-score (standardized variable) factors.

You can access the Wine and WBCD datasets from the link below:
https://github.com/abdullahelen/MachineLearning/tree/main/SVD

Main paper:
Elen, A., & Avuçlu, E. (2021). Standardized Variable Distances: A distance-based machine learning method. Applied Soft Computing, 98(2021): 106855. doi: https://doi.org/10.1016/j.asoc.2020.106855

Cite As

Abdullah Elen (2024). Standardized Variable Distances (SVD) (https://github.com/abdullahelen/MachineLearning/releases/tag/v1.0), GitHub. Retrieved April 26, 2024.

Elen, Abdullah, and Emre Avuçlu. “Standardized Variable Distances: A Distance-Based Machine Learning Method.” Applied Soft Computing, vol. 98, Elsevier BV, Jan. 2021, p. 106855, doi:10.1016/j.asoc.2020.106855.

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AGK

AdaptiveGaussianKernel

SVD

Version	Published	Release Notes
1.0	20 Dec 2020		Download

To view or report issues in this GitHub add-on, visit the GitHub Repository.

MLA	Elen, Abdullah, and Emre Avuçlu. “Standardized Variable Distances: A Distance-Based Machine Learning Method.” Applied Soft Computing, vol. 98, Elsevier BV, Jan. 2021, p. 106855, doi:10.1016/j.asoc.2020.106855.
APA	Elen, A., & Avuçlu, E. (2021). Standardized Variable Distances: A distance-based machine learning method. Applied Soft Computing, 98, 106855. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.asoc.2020.106855
BibTeX	@article{Elen_2021, doi = {10.1016/j.asoc.2020.106855}, url = {https://doi.org/10.1016%2Fj.asoc.2020.106855}, year = 2021, month = {jan}, publisher = {Elsevier {BV}}, volume = {98}, pages = {106855}, author = {Abdullah Elen and Emre Avu{\c{c}}lu}, title = {Standardized Variable Distances: A distance-based machine learning method}, journal = {Applied Soft Computing} }