Ionosphere              package: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 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. 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 Laboratory;
           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

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

        *  <URL: http://www.ics.uci.edu/~mlearn/MLRepository.html>

     and were converted to R format by
     Evgenia.Dimitriadou@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" 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).

