PROMETHEE filter-based method for microarray gene expression data

2023;
: pp. 693–702
https://doi.org/10.23939/mmc2023.03.693
Received: February 17, 2023
Accepted: July 06, 2023

Mathematical Modeling and Computing, Vol. 10, No. 3, pp. 693–702 (2023)

1
Department of Mathematics and Computer Science, Fundamental and Applied Mathematics Laboratory, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco
2
Department of Mathematics and Computer Science, Fundamental and Applied Mathematics Laboratory, Faculty of Sciences Ain Chock, Hassan II University, Casablanca, Morocco
3
Department of Mathematics and Computer Science, Fundamental and Applied Mathematics Laboratory, Faculty of Sciences Ain Chock, Hassan II University of Casablanca, Morocco

Gene expression datasets have been successfully applied for a variety of purposes, including cancer classification.  The challenges faced in developing effective classifiers for expression datasets are high dimensionality and over-fitting.  Gene selection is an effective and efficient method to overcome these challenges and improve the predictive accuracy of a classifier.  Based on PROMETHEE, this paper introduces a multi-filter ensemble approach by integrating the results of two potential filters namely MaC$\Psi$-filter and PCRWG-filter to pre-select the most informative genes.  Experiments were conducted on nine microarray datasets to demonstrate the performance of the proposed method.

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