| 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

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