Recursive Feature Elimination Based on Linear Discriminant Analysis for Molecular Selection and Classification of Diseases
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.
KeywordsGene Selection Classification LDA RFE Microarray Filter
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- 1.Alon, U., Barkai, N., et al.: Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Nat. Acad. Sci. USA (1999)Google Scholar
- 2.Alizadeh, A., Eisen, M.B., et al.: Distinct types of diffuse large (b)–cell lymphoma identified by gene expression profiling. Nature, 503–511 (2000)Google Scholar
- 3.Golub, T., Slonim, D., et al.: Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 537, 286 (1999)Google Scholar
- 5.Ye, J., Li, T., Xiong, T., Janardan, R.: Using uncorrelated discriminant analysis for tissue classification with gene expression data. IEEE/ACM Trans. Comput. 1(4), 181–190 (2004)Google Scholar
- 8.Tang, Y., Zhang, Y.-Q., Huang, Z.: Fcmsv- rfe gene feature selection algorithm for leukemia classification from microarray gene expression data. In: IEEE International Conference on Fuzzy Systems, pp. 97–10 (2005)Google Scholar
- 11.Yang, F., Mao, K.: Robust feature selection for microarray based on multicreterion fusion. IEEE/ACMTrans. Comput. Biology 8(4), 1080–1092 (2011)Google Scholar
- 12.Li, Z., Zeng, X.-Q., Yang, J.-Y., Yang, M.-Q.: Partial Least Squares based dimension reduction with gene selection for tumor classification. In: BIBE 2007, pp. 1439–1444 (2007)Google Scholar
- 13.Deng, L., Pei, J., Ma, J., Lee, D.L.: Rank sum test method for informative gene discovery. In: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), pp. 410–419 (2004)Google Scholar
- 14.Mishra, D., Sahu, B.: Feature selection for cancer classification: A signal-to-noise ratio approach. International Journal of Scientific & Engineering Research 2(4), 1–7 (2011)Google Scholar
- 19.Alba, E., García-Nieto, J., Jourdan, L., Talbi, E.-G.: Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. In: Congress on Evolutionary Computation, pages, pp. 284–290 (2007)Google Scholar
- 20.Satoshi, N., Okuno, Y.: Lapalacian linear discriminant analysis to unsupervised feature selection. IEEE/Transactions on Biology and Bioinformatics 6(4), 605–614 (2009)Google Scholar
- 21.Li, X., Peng, S., Zhan, X., Zhang, J., Xu, Y.: Comparison of feature selection methods for multiclass cancer classification based on microarray data. In: 4th International Conference on Biomedical Engineering and Informatics (BMEI), pp. 1692–1696 (2011)Google Scholar