Recursive feature elimination based on linear discriminant analysis for molecular selection and classification of diseases

Authors:
Edmundo Bonilla Huerta;Roberto Morales Caporal;Marco Antonio Arjona;José Crispín Hernández Hernández
Affiliations:
Laboratorio de Investigación en Tecnologías Inteligentes, Instituto Tecnológico de Apizaco, Apizaco, Tlaxcala, México;Laboratorio de Investigación en Tecnologías Inteligentes, Instituto Tecnológico de Apizaco, Apizaco, Tlaxcala, México;Laboratorio de Investigación en Tecnologías Inteligentes, Instituto Tecnológico de Apizaco, Apizaco, Tlaxcala, México;Laboratorio de Investigación en Tecnologías Inteligentes, Instituto Tecnológico de Apizaco, Apizaco, Tlaxcala, México
Venue:
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories and Technology
Year:
2013

Citing 9
Cited 0

Gene Selection for Cancer Classification using Support Vector Machines

Machine Learning
A rank sum test method for informative gene discovery

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Using Uncorrelated Discriminant Analysis for Tissue Classification with Gene Expression Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cancer classification using ensemble of neural networks with multiple significant gene subsets

Applied Intelligence
Gene selection using genetic algorithm and support vectors machines

Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on neural networks for pattern recognition and data mining
Laplacian Linear Discriminant Analysis Approach to Unsupervised Feature Selection

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust Feature Selection for Microarray Data Based on Multicriterion Fusion

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Informative gene selection and tumor classification by null space LDA for microarray data

ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose an effective Recursive Feature Elimination based on Linear Discriminant Analysis (RFELDA) method for gene selection and classification of diseases obtained from DNA microarray technology. LDA is proposed not only as an LDA classifier, but also as an LDA's discriminant coefficients to obtain ranks for each gene. The performance of the proposed algorithm was tested against four well-known datasets from the literature and compared with recent state of the art algorithms. The experiment results on these datasets show that RFELDA outperforms similar methods reported in the literature, and obtains high classification accuracies with a relatively small number of genes.