Abstract
The curse of dimensionality is one of the well known issues in Biological data bases. A possible solution to avoid this issue is to use feature selection approach. Filter feature selection are well know feature selection methods that selects the most significant features and discards the rest according to their significance level. In general The set of eliminated features may hide some useful information that may be valuable in further studies. Hence, this paper present a new approach for filter feature selection that uses redundant features to create new instances and avoid the curse of dimensionality.
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References
Bellman, R.: Processus Adaptive Control: A Guided Tour. Princeton University Press, Princeton (1961)
Guerif, S.: Rduction de dimension en apprentissage numrique non supervise. Ph.D. thesis, Universit Paris 13 (2006)
Salvador, G., Julin, L., Francisco, H.: Data preprocessing in Data Mining. In: Kacprzyk, J., (ed.) Polish Academy of Sciences. Springer, Poland, Warsaw (2015)
Pomeroy, S.L., Tamayo, P., Gaasenbeek, M., Sturla, L.M., Angelo, M., McLaughlin, M.E., Kim, J.Y.H., Goumnerova, L.C., Black, P.M., Lau, C., Allen, J.C., Zagzag, D., Olson, J.M., Curran, T., Wetmore, C., Biegel, J.A., Poggio, T., Mukherjee, S., Rifkin, R., Califano, A., Stolovitzky, G., Louis, D.N., Mesirov, J.P., Lander, E.S., Golub, T.R.: Prediction of central nervous system embryonal tumour outcome based on gene expression. Nat. 415(6870), 436–442 (2002)
Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Sci. 286, 531–537 (1999)
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Mouelhi, E., Bouaguel, W., Bel Mufti, G. (2015). A Redundancy Study for Feature Selection in Biological Data. In: Prasath, R., Vuppala, A., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2015. Lecture Notes in Computer Science(), vol 9468. Springer, Cham. https://doi.org/10.1007/978-3-319-26832-3_3
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DOI: https://doi.org/10.1007/978-3-319-26832-3_3
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