Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!
Get Started for Free

Sonar

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)

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 at

and were converted to R format by Evgenia Dimitriadou.

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.

Newman, D.J. & Hettich, S. & Blake, C.L. & Merz, C.J. (1998). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.

Examples

data(Sonar)
summary(Sonar)

mlbench

Machine Learning Benchmark Problems

v2.1-3
GPL-2
Authors
Friedrich Leisch and Evgenia Dimitriadou.
Initial release
2021-01-21

We don't support your browser anymore

Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.