\HeaderA{Ionosphere}{Johns Hopkins University Ionosphere database}{Ionosphere}
\keyword{datasets}{Ionosphere}
\begin{Description}\relax
This radar data was collected by a system in Goose Bay, Labrador.  This
system consists of a phased array of 16 high-frequency antennas with a
total transmitted power on the order of 6.4 kilowatts.  See the paper
for more details.  The targets were free electrons in the ionosphere.
"good" radar returns are those showing evidence of some type of structure 
in the ionosphere.  "bad" returns are those that do not; their signals pass
through the ionosphere.  

Received signals were processed using an autocorrelation function whose
arguments are the time of a pulse and the pulse number.  There were 17
pulse numbers for the Goose Bay system.  Instances in this databse are
described by 2 attributes per pulse number, corresponding to the complex
values returned by the function resulting from the complex electromagnetic
signal. See cited below for more details.
\end{Description}
\begin{Usage}
\begin{verbatim}data(Ionosphere)\end{verbatim}
\end{Usage}
\begin{Format}\relax
A data frame with 351 observations on 35 independent variables, some 
numerical and 2 nominal, and one last defining the class.
\end{Format}
\begin{Source}\relax
\Itemize{
\item Source: Space Physics Group; Applied Physics Laboratory;
Johns Hopkins University; Johns Hopkins Road; Laurel; MD 20723 
\item Donor: Vince Sigillito (vgs@aplcen.apl.jhu.edu)
}
These data have been taken from the UCI Repository Of Machine Learning
Databases at
\Itemize{
\item \url{ftp://ftp.ics.uci.edu/pub/machine-learning-databases}
\item \url{http://www.ics.uci.edu/~mlearn/MLRepository.html}
}
and were converted to R format by Evgenia.Dimitriadou@ci.tuwien.ac.at.
\end{Source}
\begin{References}\relax
Sigillito, V. G., Wing, S. P., Hutton, L. V., \& Baker, K. B. (1989).
Classification of radar returns from the ionosphere using neural 
networks. Johns Hopkins APL Technical Digest, 10, 262-266.

They investigated using backprop and the perceptron training algorithm
on this database.  Using the first 200 instances for training, which
were carefully split almost 50\% positive and 50\% negative, they found
that a "linear" perceptron attained 90.7\%, a "non-linear" perceptron
attained 92\%, and backprop an average of over 96\% accuracy on the 
remaining 150 test instances, consisting of 123 "good" and only 24 "bad"
instances.  (There was a counting error or some mistake somewhere; there
are a total of 351 rather than 350 instances in this domain.) Accuracy
on "good" instances was much higher than for "bad" instances.  Backprop
was tested with several different numbers of hidden units (in [0,15])
and incremental results were also reported (corresponding to how well
the different variants of backprop did after a periodic number of 
epochs).

David Aha (aha@ics.uci.edu) briefly investigated this database.
He found that nearest neighbor attains an accuracy of 92.1\%, that
Ross Quinlan's C4 algorithm attains 94.0\% (no windowing), and that
IB3 (Aha \& Kibler, IJCAI-1989) attained 96.7\% (parameter settings:
70\% and 80\% for acceptance and dropping respectively).
\end{References}

