Abstract
Many real-world classification problems involve very sparse and high-dimensional data. The successes of LIKNON - linear programming support vector machine (LPSVM) for feature selection, motivates a more thorough analysis of the method when applied to sparse, multivariate data. Due to the sparseness, the selection of a classification model is greatly influenced by the characteristics of that particular dataset. Robust feature/model selection methods are desirable. LIKNON is claimed to have such robustness properties. Its feature selection operates by selecting the groups of features with large differences between the resultants of the two classes. The degree of desired difference is controlled by the regularization parameter. We study the practical value of LIKNON-based feature/model selection for microarray data. Our findings support the claims about the robustness of the method.
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References
Ambroise, C., McLachlan, G.J.: Selection bias in gene extraction on the basis of microarray gene-expression data. PNAS. 99(10), 6562–6566 (2002)
Bhattacharyya, C., Grate, L.R., Rizki, A., et al.: Simultaneous relevant feature identification and classification in high-dimensional spaces: application to molecular profiling data. Signal Processing 83(4), 729–743 (2003)
Davis, C.A., Gerick, F., Hintermair, V., et al.: Reliable gene signatures for microarray classification: assessment of stability and performance. Bioinformatics 22(19), 2356–2363 (2006)
Berrar, D.P., Bradbury, I., Dubitzky, W.: Avoiding model selection bias in small-sample genomic datasets. Bioinformatics 22(10), 1245–1250 (2006)
Filippone, M., Masulli, F., Rovetta, S.: Supervised classification and gene selection using simulated annealing. In: Proc. Int. Joint Conf. on Neural Networks, pp. 6872–6877 (2006)
Fung, G., Mangasarian, O.: A feature selection Newton method for support vector machine classification. Computational Optimization and Applications 28, 185–202 (2004)
Guo, G-D., Dyer, C.: Learning from examples in the small sample case: face expression recognition. IEEE Trans. on System, Man and Cybernetics - Part B 35(3), 477–488 (2005)
Guyon, I., Gunn, S., Nikravesh, M., Zadeh, L. (eds.): Feature extraction, foundations and applications. Springer, Heidelberg (2006)
Kecman, V., Huang, T.M.: Gene extraction for cancer diagnosis by support vector machines. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS, vol. 3696, pp. 617–624. Springer, Heidelberg (2005)
Kent Ridge Bio-Medical data repository, http://sdmc.lit.org.sg/GEDatasets
Ng, A.: Feature selection, L 1 vs. L 2 regularization, and rotational invariance. In: Proc. 21st Int. Conf. on Machine learning, Morgan-Kaufman, Seattle, Washington, USA (2004)
Pranckeviciene, E., Ho, T.K., Somorjai, R.L.: Class separability in spaces reduced by feature selection. In: Int. Conf. on Pattern Recognition. vol. 3, pp. 254–257 (2006)
Pranckeviciene, E., Somorjai, R.: On classification models of gene expression microarrays: the simpler the better. In: Proc. Int. Joint Conf. on Neural Networks, pp. 6878–6885 (2006)
Pranckeviciene, E., Somorjai, R., Baumgartner, R., Jeon, M.: Identification of signatures in biomedical spectra using domain knowledge. AI in Medicine 35(3), 215–226 (2005)
Raudys, S., Baumgartner, R., Somorjai, R.: On understanding and assessing feature selection bias. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds.) AIME 2005. LNCS (LNAI), vol. 3581, pp. 468–472. Springer, Heidelberg (2005)
Somorjai, R., Dolenko, B., Baumgartner, R.: Class prediction and discovery using gene microarray and proteomics mass spectroscopy data: curses, cavets, cautions. Bioinformatics 19(12), 1484–1491 (2003)
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Pranckeviciene, E., Somorjai, R. (2007). Liknon Feature Selection for Microarrays. In: Masulli, F., Mitra, S., Pasi, G. (eds) Applications of Fuzzy Sets Theory. WILF 2007. Lecture Notes in Computer Science(), vol 4578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73400-0_74
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DOI: https://doi.org/10.1007/978-3-540-73400-0_74
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