Discriminant analysis is part of statistical learning; its goal is to separate classes defined a priori on a population and involves predicting the class of given data points. Discriminant analysis is applied in various fields such as pattern recognition, DNA microarray etc. In recent years, the discrimination problem remains a challenging task that has received increasing attention, especially for high-dimensional data sets. Indeed, in such a case, the feature selection is necessary, which implies the use of criteria of relevance, redundancy and complementarity of explanatory variables. The aim of this paper is to present an analysis of three new criteria proposed in this sense, more precisely based on the Principal Component Analysis we have been able to achieve a double objective: that of studying the harmony of these three criteria and also visualizing the class of candidate variables for a more in-depth selection in addition to eliminating the noise variables in a discriminant model.
- Chah Slaoui S., Chamlal H. Nouvelles approches pour la sélection de variables discriminantes. Revue de statistique appliquée. 48 (4), 59–82 (2000).
- Chamlal H., Ouaderhman T., Aaboub F. A graph based preordonnances theoretic supervised feature selection in high dimensional data. Knowledge-Based Systems. 257, 109899 (2022).
- Chamlal H., Ouaderhman T., El Mourtji B. Feature selection in high dimensional data: A specific preordonnances-based memetic algorithm. Knowledge-Based Systems. 266, 110420 (2023).
- Chamlal H., Ouaderhman T., El Mourtji B. Multicriteria approaches based on a new discrimination criterions for feature selection. In: 2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS). 1–7 (2021).
- Chamlal H., Ouaderhman T., Rebbah F. E. A hybrid feature selection approach for Microarray datasets using graph theoretic-based method. Information Sciences. 615, 449–474 (2022).
- Chen Z., Chen Q., Zhang Y., Zhou L., Jiang J., Wu C., Huang Z. Clustering-based feature subset selection with analysis on the redundancy–complementarity dimension. Computer Communications. 168, 65–74 (2021).
- Chen Z., Wu C., Zhang Y., Huang Z., Bin R., Ming Z., Nengchao L. Feature selection with redundancy-complementariness dispersion. Knowledge-Based Systems. 89, 203–217 (2015).
- Ferreira A. J., Figueiredo M. A. T. Efficient feature selection filters for high-dimensional data. Pattern Recognition Letters. 33 (13), 1794–1804 (2012).
- John G. H., Kohavi R., Pfleger K. Irrelevant Features and the Subset Selection Problem. In: Machine Learning Proceedings 1994. 121–129 (1994).
- Kurita T. Principal Component Analysis (PCA). In: Computer Vision: A Reference Guide. 1–4 (2019).
- Radovic M., Ghalwash M., Filipovic N., Obradovic Z. Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinformatics. 18 (1), 9 (2017).
- Singha S., Shenoy P. P. An adaptive heuristic for feature selection based on complementarity. Machine Learning. 107 (12), 2027–2071 (2018).
- Souza F., Premebida C., Araújo R. High-order conditional mutual information maximization for dealing with high-order dependencies in feature selection. Pattern Recognition. 131, 108895 (2022).
- Zhou H., Zhang Y., Zhang Y., Liu H. Feature selection based on conditional mutual information: minimum conditional relevance and minimum conditional redundancy. Applied Intelligence. 49 (3), 883–896 (2019).