 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, 262266.

 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 "nonlinear" 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, IJCAI1989) 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 highfrequency 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
g,b
antenna1: continuous
antenna2: continuous
antenna3: continuous
antenna4: continuous
antenna5: continuous
antenna6: continuous
antenna7: continuous
antenna8: continuous
antenna9: continuous
antenna10: continuous
antenna11: continuous
antenna12: continuous
antenna13: continuous
antenna14: continuous
antenna15: continuous
antenna16: continuous
antenna17: continuous
antenna18: continuous
antenna19: continuous
antenna20: continuous
antenna21: continuous
antenna22: continuous
antenna23: continuous
antenna24: continuous
antenna25: continuous
antenna26: continuous
antenna27: continuous
antenna28: continuous
antenna29: continuous
antenna30: continuous
antenna31: continuous
antenna32: continuous
antenna33: continuous
antenna34: continuous