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.
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Huerta, E.B., Caporal, R.M., Arjona, M.A., Hernández, J.C.H. (2013). Recursive Feature Elimination Based on Linear Discriminant Analysis for Molecular Selection and Classification of Diseases. In: Huang, DS., Jo, KH., Zhou, YQ., Han, K. (eds) Intelligent Computing Theories and Technology. ICIC 2013. Lecture Notes in Computer Science(), vol 7996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39482-9_28
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DOI: https://doi.org/10.1007/978-3-642-39482-9_28
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