Distance Rank Score: Unsupervised filter method for feature selection on imbalanced dataset
Machine Learning
2023-06-01 v1 Machine Learning
Abstract
This paper presents a new filter method for unsupervised feature selection. This method is particularly effective on imbalanced multi-class dataset, as in case of clusters of different anomaly types. Existing methods usually involve the variance of the features, which is not suitable when the different types of observations are not represented equally. Our method, based on Spearman's Rank Correlation between distances on the observations and on feature values, avoids this drawback. The performance of the method is measured on several clustering problems and is compared with existing filter methods suitable for unsupervised data.
Cite
@article{arxiv.2305.19804,
title = {Distance Rank Score: Unsupervised filter method for feature selection on imbalanced dataset},
author = {Katarina Firdova and Céline Labart and Arthur Martel},
journal= {arXiv preprint arXiv:2305.19804},
year = {2023}
}