1. MMC
  2. All Volumes and Issues
  3. Volume 10, Number 3, 2023
  4. PROMETHEE filter-based method for microarray gene expression data

PROMETHEE filter-based method for microarray gene expression data

MMC.
2023;
Volume 10, Number 3
: 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)

Authors:
  1. T. Ouaderhman
  2. F. Aaboub
  3. H. Chamlal
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.

filter
classification
selection
microarray data
PROMETHEE
  1. Ang J. C., Mirzal A., Haron H., Hamed H. N. A.  Upervised, unsupervised, and semi-supervised feature selection: a review on gene selection.  IEEE/ACM Transactions on Computational Biology and Bioinformatics.  13 (5), 971–989 (2015).
  2. Battiti R.  Using mutual information for selecting features in supervised neural net learning.  IEEE Transactions on neural networks.  5 (4), 537–550 (1994).
  3. Alhaj T. A., Siraj M. M., Zainal A., Elshoush H. T., Elhaj F.  Feature selection using information gain for improved structural-based alert correlation.  PloS One.  11, e0166017 (2016).
  4. Karegowda A. G., Manjunath A. S.,Jayaram M. A.  Comparative study of attribute selection using gain ratio and correlation based feature selection.  International Journal of Information Technology and Knowledge Management.  2 (2), 271–277 (2010).
  5. Sun L., Wang T., Ding W., Xu J., Lin Y.  Feature selection using Fisher score and multilabel neighborhood rough sets for multilabel classification.  Information Sciences.  578, 887–912 (2021).
  6. Javandel V., Vakilian M., Firuzi K.  Multiple partial discharge sources separation using a method based on laplacian score and correlation coefficient techniques.  Electric Power Systems Research.  210, 108070 (2022).
  7. Ahakonye L. A. C., Nwakanma C. I., Lee J.-M., Kim D. S.  SCADA intrusion detection scheme exploiting the fusion of modified decision tree and Chi-square feature selection.  Internet of Things.  21, 100676 (2023).
  8. Potharaju S. P., Sreedevi M.  Distributed feature selection (DFS) strategy for microarray gene expression data to improve the classification performance.  Clinical Epidemiology and Global Health.  7 (2), 171–176 (2019).
  9. Yu L., Liu H.  Feature selection for high-dimensional data: A fast correlation-based filter solution.  Proceedings of the 20th international conference on machine learning (ICML-03). 856–863 (2003).
  10. Shreem S. S., Abdullah S., Nazri M. Z. A., Alzaqebah M.  Hybridizing ReliefF, MRMR filters and GA wrapper approaches for gene selection.  Journal of Theoretical and Applied Information Technology.  46 (2), 1034–1039 (2012).
  11. Radovic M., Ghalwash M., Filipovic N., Obradovic Z.  Minimum redundancy maximum relevance feature selection approach for temporal gene expression data.  BMC Bioinformatics.  18, 9 (2017).
  12. Chamlal H., Ouaderhman T., Aaboub F.  A graph based preordonnances theoretic supervised feature selection in high dimensional data.  Knowledge-Based Systems.  257, 109899 (2022).
  13. 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).
  14. 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).
  15. Venkata Rao R., Patel B. K.  Decision making in the manufacturing environment using an improved PROMETHEE method.  International Journal of Production Research.  48 (16), 4665–4682 (2010).
  16. Vapnik V.  The Nature of Statistical Learning Theory.  Springer Science & Business Media, New York (1999).
  17. Rish I.  An empirical study of the naive Bayes classifier.  IJCAI 2001 workshop on empirical methods in artificial intelligence.  3, 41–46 (2001).
  18. Cover T., Hart P.  Nearest neighbor pattern classification.  IEEE Transactions on Information Theory.  13, 21 (1967).
  19. Ding C., Peng H.  Minimum redundancy feature selection from microarray gene expression data.  Journal of Bioinformatics and Computational Biology.  3 (2), 185–205 (2005).
  20. Demšar J.  Statistical comparisons of classifiers over multiple data sets.  The Journal of Machine Learning Research.  7, 1–30 (2006).