Abstract
In this paper, we introduce a new approach referred to as Essential Attributes Generation (EAG) to reduce the dimensionality of multidimensional real-valued data series. We form a new representation of the original data. The approach is based on the concept of essential attributes generated by a multilayer neural network. The EAG generates a vector of real valued new attributes which form the compressed representation of the original data. The attributes are synthetic, and while not being directly interpretable, they still retain important features of the original data series. The approach has found applications to classification as well as clustering tasks.
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Krawczak, M., Szkatuła, G. (2014). Essential Attributes Generation for Some Data Mining Tasks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2014. Lecture Notes in Computer Science(), vol 8468. Springer, Cham. https://doi.org/10.1007/978-3-319-07176-3_5
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DOI: https://doi.org/10.1007/978-3-319-07176-3_5
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07175-6
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