

Ionosphere(mlbench)                          R Documentation

_J_o_h_n_s _H_o_p_k_i_n_s _U_n_i_v_e_r_s_i_t_y _I_o_n_o_s_p_h_e_r_e _d_a_t_a_b_a_s_e

_D_e_s_c_r_i_p_t_i_o_n_:

     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 iono-
     sphere.  "good" radar returns are those showing evi-
     dence 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 autocorrela-
     tion 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, correspond-
     ing to the complex values returned by the function
     resulting from the complex electromagnetic signal. See
     cited below for more details.

_U_s_a_g_e_:

     data(Ionosphere)

_F_o_r_m_a_t_:

     A data frame with 351 observations on 35 independent
     variables, some numerical and 2 nominal, and one last
     defining the class.

_S_o_u_r_c_e_:

        * Source: Space Physics Group; Applied Physics Labo-
          ratory; Johns Hopkins University; Johns Hopkins
          Road; Laurel; MD 20723

        * Donor: Vince Sigillito (vgs@aplcen.apl.jhu.edu)

     These data have been taken from the UCI Repository Of
     Machine Learning Databases at

        * ftp.ics.uci.edu://pub/machine-learning-databases

        * http://www.ics.uci.edu/mlearn/MLRepository.html

     and were converted to R format by Evgenia.Dimitri-
     adou@ci.tuwien.ac.at.

_R_e_f_e_r_e_n_c_e_s_:

     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" per-
     ceptron attained 92%, and backprop an average of over
     96% accuracy on the remaining 150 test instances, con-
     sisting 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).

