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66 lines
3.0 KiB
Plaintext
Executable File
66 lines
3.0 KiB
Plaintext
Executable File
1. Title: Johns Hopkins University Ionosphere database
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2. Source Information:
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-- Donor: Vince Sigillito (vgs@aplcen.apl.jhu.edu)
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-- Date: 1989
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-- Source: Space Physics Group
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Applied Physics Laboratory
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Johns Hopkins University
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Johns Hopkins Road
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Laurel, MD 20723
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3. Past Usage:
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-- Sigillito, V. G., Wing, S. P., Hutton, L. V., \& Baker, K. B. (1989).
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Classification of radar returns from the ionosphere using neural
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networks. Johns Hopkins APL Technical Digest, 10, 262-266.
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They investigated using backprop and the perceptron training algorithm
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on this database. Using the first 200 instances for training, which
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were carefully split almost 50% positive and 50% negative, they found
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that a "linear" perceptron attained 90.7%, a "non-linear" perceptron
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attained 92%, and backprop an average of over 96% accuracy on the
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remaining 150 test instances, consisting of 123 "good" and only 24 "bad"
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instances. (There was a counting error or some mistake somewhere; there
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are a total of 351 rather than 350 instances in this domain.) Accuracy
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on "good" instances was much higher than for "bad" instances. Backprop
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was tested with several different numbers of hidden units (in [0,15])
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and incremental results were also reported (corresponding to how well
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the different variants of backprop did after a periodic number of
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epochs).
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David Aha (aha@ics.uci.edu) briefly investigated this database.
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He found that nearest neighbor attains an accuracy of 92.1%, that
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Ross Quinlan's C4 algorithm attains 94.0% (no windowing), and that
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IB3 (Aha \& Kibler, IJCAI-1989) attained 96.7% (parameter settings:
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70% and 80% for acceptance and dropping respectively).
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4. Relevant Information:
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This radar data was collected by a system in Goose Bay, Labrador. This
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system consists of a phased array of 16 high-frequency antennas with a
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total transmitted power on the order of 6.4 kilowatts. See the paper
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for more details. The targets were free electrons in the ionosphere.
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"Good" radar returns are those showing evidence of some type of structure
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in the ionosphere. "Bad" returns are those that do not; their signals pass
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through the ionosphere.
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Received signals were processed using an autocorrelation function whose
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arguments are the time of a pulse and the pulse number. There were 17
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pulse numbers for the Goose Bay system. Instances in this databse are
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described by 2 attributes per pulse number, corresponding to the complex
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values returned by the function resulting from the complex electromagnetic
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signal.
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5. Number of Instances: 351
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6. Number of Attributes: 34 plus the class attribute
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-- All 34 predictor attributes are continuous
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7. Attribute Information:
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-- All 34 are continuous, as described above
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-- The 35th attribute is either "good" or "bad" according to the definition
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summarized above. This is a binary classification task.
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8. Missing Values: None
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