Abstract
We consider decision tables with real value conditional attributes and we present a method for extraction of features defined by hyperplanes in a multi-dimensional affine space. These new features are often more relevant for object classification than the features defined by hyperplanes parallel to axes. The method generalizes an approach presented in [18] in case of hyperplanes not necessarily parallel to the axes. We propose genetic strategies searching for hyperplanes discerning between objects from different decision classes.
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© 1996 Springer-Verlag Berlin Heidelberg
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Son, N.H., Hoa, N.S., Skowron, A. (1996). Searching for features defined by hyperplanes. In: Raś, Z.W., Michalewicz, M. (eds) Foundations of Intelligent Systems. ISMIS 1996. Lecture Notes in Computer Science, vol 1079. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61286-6_161
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DOI: https://doi.org/10.1007/3-540-61286-6_161
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