| Sonar {mlbench} | R Documentation |
Sonar, Mines vs. Rocks
Description
This is the data set used by Gorman and Sejnowski in their study of the classification of sonar signals using a neural network [1]. The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.
Each pattern is a set of 60 numbers in the range 0.0 to 1.0. Each number represents the energy within a particular frequency band, integrated over a certain period of time. The integration aperture for higher frequencies occur later in time, since these frequencies are transmitted later during the chirp.
The label associated with each record contains the letter "R" if the object is a rock and "M" if it is a mine (metal cylinder). The numbers in the labels are in increasing order of aspect angle, but they do not encode the angle directly.
Usage
data("Sonar", package = "mlbench")
Format
A data frame with 208 observations on 61 variables, all numerical and one (the Class) nominal.
Source
Contribution: Terry Sejnowski, Salk Institute and University of California, San Deigo.
Development: R. Paul Gorman, Allied-Signal Aerospace Technology Center.
Maintainer: Scott E. Fahlman
These data have been taken from the UCI Repository Of Machine Learning Databases (Blake & Merz 1998) and were converted to R format by Evgenia Dimitriadou in the late 1990s.
The current version of the UC Irvine Machine Learning Repository Connectionist Bench Sonar, Mines vs. Rocks data set is available from doi:10.24432/C5T01Q.
References
Gorman, R. P., and Sejnowski, T. J. (1988). "Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets" in Neural Networks, Vol. 1, pp. 75-89.
Blake, C.L. & Merz, C.J. (1998). UCI Repository of Machine Learning Databases. Irvine, CA: University of California, Irvine, Department of Information and Computer Science. Formerly available from ‘http://www.ics.uci.edu/~mlearn/MLRepository.html’.
Examples
data("Sonar", package = "mlbench")
summary(Sonar)