Abstract
The problem of selection of variables seems to be the key issue in classification of multi-dimensional objects. An optimal set of features should be made of only those variables, which are essential for the differentiation of studied objects. This selection may be made easier if a graphic analysis of an U-matrix is carried out. It allows to easily identify variables, which do not differentiate the studied objects. A graphic analysis may, however, not suffice to analyse data when an object is described with hundreds of variables. The authors of the paper propose a procedure which allows to eliminate variables with the smallest discriminating potential based on the measurement of concentration of objects on the Kohonen self organising map networks.
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Najman, K.M., Najman, K. (2008). Applying the Kohonen Self-Organizing Map Networks to Select Variables. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds) Data Analysis, Machine Learning and Applications. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78246-9_6
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DOI: https://doi.org/10.1007/978-3-540-78246-9_6
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