ionosphere.arff

%1. Title: Johns Hopkins University Ionosphere database
%
%2. Source Information:
%   -- Donor: Vince Sigillito (vgs@aplcen.apl.jhu.edu)
%   -- Date: 1989
%   -- Source: Space Physics Group
%              Applied Physics Laboratory
%              Johns Hopkins University
%              Johns Hopkins Road
%              Laurel, MD 20723 
%
%3. Past Usage:
%   -- 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).
%
%4. Relevant Information:
%   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.
%
%5. Number of Instances: 351
%
%6. Number of Attributes: 34 plus the class attribute
%   -- All 34 predictor attributes are continuous
%
%7. Attribute Information:     
%   -- All 34 are continuous, as described above
%   -- The 35th attribute is either "good" or "bad" according to the definition
%      summarized above.  This is a binary classification task.
%
%8. Missing Values: None

@relation ionosphere

@attribute a01 real
@attribute a02 real
@attribute a03 real
@attribute a04 real
@attribute a05 real
@attribute a06 real
@attribute a07 real
@attribute a08 real
@attribute a09 real
@attribute a10 real
@attribute a11 real
@attribute a12 real
@attribute a13 real
@attribute a14 real
@attribute a15 real
@attribute a16 real
@attribute a17 real
@attribute a18 real
@attribute a19 real
@attribute a20 real
@attribute a21 real
@attribute a22 real
@attribute a23 real
@attribute a24 real
@attribute a25 real
@attribute a26 real
@attribute a27 real
@attribute a28 real
@attribute a29 real
@attribute a30 real
@attribute a31 real
@attribute a32 real
@attribute a33 real
@attribute a34 real
@attribute class {b, g}

@data

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