Standardized Variable Distances (SVD)

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

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