\name{Ionosphere}
\title{Johns Hopkins University Ionosphere database}
\usage{
data("Ionosphere", package = "mlbench")
}
\alias{Ionosphere}
\format{A data frame with 351 observations on 35 independent variables, some 
    numerical and 2 nominal, and one last defining the class.}

\description{
    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.}
\source{
    \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 \bibcitep{Blake+Merz:1998} and were converted to
    R format by Evgenia Dimitriadou in the late 1990s.

    The current version of the UC Irvine Machine Learning Repository
    Ionosphere data set is available from \doi{10.24432/C5W01B}.
}
\references{
  \bibshow{Sigillito+Wing+Hutton:1989}

      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 and Kibler, IJCAI-1989) attained 96.7\% (parameter settings:
      70\% and 80\% for acceptance and dropping respectively).

  \bibshow{Blake+Merz:1998}
}
\keyword{datasets}
\examples{
data("Ionosphere", package = "mlbench")
summary(Ionosphere)
}
    
