Clustering-based Sequential Feature Selection Approach for High Dimensional Data Classification

M. Alimoussa, M. Alimoussa, A. Porebski, N. Vandenbroucke, R. Oulad Haj Thami, S. El Fkihi

2021

Abstract

Feature selection has become the focus of many research applications specially when datasets tend to be huge. Recently, approaches that use feature clustering techniques have gained much attention for their ability to improve the selection process. In this paper, we propose a clustering-based sequential feature selection approach based on a three step filter model. First, irrelevant features are removed. Then, an automatic feature clustering algorithm is applied in order to divide the feature set into a number of clusters in which features are redundant or correlated. Finally, one feature is sequentially selected per group. Two experiments are conducted, the first one using six real wold numerical data and the second one using features extracted from three color texture image datasets. Compared to seven feature selection algorithms, the obtained results show the effectiveness and the efficiency of our approach.

Paper Citation

in Harvard Style

Alimoussa M., Porebski A., Vandenbroucke N., Thami R. and El Fkihi S. (2021). Clustering-based Sequential Feature Selection Approach for High Dimensional Data Classification. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP; ISBN 978-989-758-488-6, SciTePress, pages 122-132. DOI: 10.5220/0010259501220132

in Bibtex Style

@conference{visapp21,
author={M. Alimoussa and A. Porebski and N. Vandenbroucke and R. Oulad Haj Thami and S. El Fkihi},
title={Clustering-based Sequential Feature Selection Approach for High Dimensional Data Classification},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP},
year={2021},
pages={122-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010259501220132},
isbn={978-989-758-488-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2021) - Volume 4: VISAPP
TI - Clustering-based Sequential Feature Selection Approach for High Dimensional Data Classification
SN - 978-989-758-488-6
AU - Alimoussa M.
AU - Porebski A.
AU - Vandenbroucke N.
AU - Thami R.
AU - El Fkihi S.
PY - 2021
SP - 122
EP - 132
DO - 10.5220/0010259501220132
PB - SciTePress