| IchinoFS.SDA {symbolicDA} | R Documentation |
Ichino's feature selection method for symbolic data
Description
Ichino's method for identifiyng non-noisy variables in symbolic data set
Usage
IchinoFS.SDA(table.Symbolic)
Arguments
table.Symbolic |
symbolic data table |
Details
See file ../doc/IchinoFSSDA_details.pdf for further details
Value
plot |
plot of the gradient illustrating combinations of variables, in which the axis of ordinates (Y) represents the maximum number of mutual neighbor pairs and the axis of the abscissae (X) corresponds to the number of features (m) |
combination |
the best combination of variables, i.e. the combination most differentiating the set of objects |
maximum results |
step-by-step combinations of variables up to m variables |
calculation results |
.............. |
Author(s)
Andrzej Dudek andrzej.dudek@ue.wroc.pl, Justyna Wilk justyna.wilk@ue.wroc.pl Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland http://keii.ue.wroc.pl/symbolicDA/
References
Ichino, M. (1994), Feature selection for symbolic data classification, In: E. Diday, Y. Lechevallier, P.B. Schader, B. Burtschy (Eds.), New Approaches in Classification and data analysis, Springer-Verlag, pp. 423-429.
Bock, H.H., Diday, E. (eds.) (2000), Analysis of symbolic data. Explanatory methods for extracting statistical information from complex data, Springer-Verlag, Berlin.
Diday, E., Noirhomme-Fraiture, M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester.
See Also
HINoV.SDA; HINoV.Symbolic in clusterSim library
Examples
# LONG RUNNING - UNCOMMENT TO RUN
#data("cars",package="symbolicDA")
#sdt<-cars
#ichino<-IchinoFS.SDA(sdt)
#print(ichino)