sonar: Sonar, Mines vs. Rocks.

In ESKNN: Ensemble of Subset of K-Nearest Neighbours Classifiers for Classification and Class Membership Probability Estimation

Description

This data set is a collection of sonar signals, coded as 60 continuous attributes on 208 observations. The sonar signals are obtained from a variety of different aspect angles, spanning 90 degrees for mines and 180 degrees for rocks. The task is classification of sonar signals in two catagories, signals bounced off a "rock" or a "metal cylinder". Each pattern in the data is a set of 60 numbers (continous) in the range 0.0 to 1.0, where each number represents the energy within a particular frequency band, integrated over a certain period of time. From total 208 observations, 111 obtained by bouncing sonar signals off a metal cylinder at various angles and under various conditions, is labled with "M" and 97 patterns obtained from rocks under similar conditions is labled with "R".

Usage

data(sonar)

Format

A data frame with 208 observations on 60 features/attributes in two classes. All the features are numerical and the class is nominal.

Source

This data set is available on: ftp://ftp.ics.uci.edu/pub/machine-learning-databases http://sci2s.ugr.es/keel/dataset.php?cod=85

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.

Friedrich Leisch & Evgenia Dimitriadou (2010). mlbench: Machine Learning Benchmark Problems. R package version 2.1-1.

Examples

data(sonar)
str(sonar) 

Example output

'data.frame':	208 obs. of  61 variables:
 $ V1   : num  0.02 0.0453 0.0262 0.01 0.0762 0.0286 0.0317 0.0519 0.0223 0.0164 ...
 $ V2   : num  0.0371 0.0523 0.0582 0.0171 0.0666 0.0453 0.0956 0.0548 0.0375 0.0173 ...
 $ V3   : num  0.0428 0.0843 0.1099 0.0623 0.0481 ...
 $ V4   : num  0.0207 0.0689 0.1083 0.0205 0.0394 ...
 $ V5   : num  0.0954 0.1183 0.0974 0.0205 0.059 ...
 $ V6   : num  0.0986 0.2583 0.228 0.0368 0.0649 ...
 $ V7   : num  0.154 0.216 0.243 0.11 0.121 ...
 $ V8   : num  0.16 0.348 0.377 0.128 0.247 ...
 $ V9   : num  0.3109 0.3337 0.5598 0.0598 0.3564 ...
 $ V10  : num  0.211 0.287 0.619 0.126 0.446 ...
 $ V11  : num  0.1609 0.4918 0.6333 0.0881 0.4152 ...
 $ V12  : num  0.158 0.655 0.706 0.199 0.395 ...
 $ V13  : num  0.2238 0.6919 0.5544 0.0184 0.4256 ...
 $ V14  : num  0.0645 0.7797 0.532 0.2261 0.4135 ...
 $ V15  : num  0.066 0.746 0.648 0.173 0.453 ...
 $ V16  : num  0.227 0.944 0.693 0.213 0.533 ...
 $ V17  : num  0.31 1 0.6759 0.0693 0.7306 ...
 $ V18  : num  0.3 0.887 0.755 0.228 0.619 ...
 $ V19  : num  0.508 0.802 0.893 0.406 0.203 ...
 $ V20  : num  0.48 0.782 0.862 0.397 0.464 ...
 $ V21  : num  0.578 0.521 0.797 0.274 0.415 ...
 $ V22  : num  0.507 0.405 0.674 0.369 0.429 ...
 $ V23  : num  0.433 0.396 0.429 0.556 0.573 ...
 $ V24  : num  0.555 0.391 0.365 0.485 0.54 ...
 $ V25  : num  0.671 0.325 0.533 0.314 0.316 ...
 $ V26  : num  0.641 0.32 0.241 0.533 0.229 ...
 $ V27  : num  0.71 0.327 0.507 0.526 0.7 ...
 $ V28  : num  0.808 0.277 0.853 0.252 1 ...
 $ V29  : num  0.679 0.442 0.604 0.209 0.726 ...
 $ V30  : num  0.386 0.203 0.851 0.356 0.472 ...
 $ V31  : num  0.131 0.379 0.851 0.626 0.51 ...
 $ V32  : num  0.26 0.295 0.504 0.734 0.546 ...
 $ V33  : num  0.512 0.198 0.186 0.612 0.288 ...
 $ V34  : num  0.7547 0.2341 0.2709 0.3497 0.0981 ...
 $ V35  : num  0.854 0.131 0.423 0.395 0.195 ...
 $ V36  : num  0.851 0.418 0.304 0.301 0.418 ...
 $ V37  : num  0.669 0.384 0.612 0.541 0.46 ...
 $ V38  : num  0.61 0.106 0.676 0.881 0.322 ...
 $ V39  : num  0.494 0.184 0.537 0.986 0.283 ...
 $ V40  : num  0.274 0.197 0.472 0.917 0.243 ...
 $ V41  : num  0.051 0.167 0.465 0.612 0.198 ...
 $ V42  : num  0.2834 0.0583 0.2587 0.5006 0.2444 ...
 $ V43  : num  0.282 0.14 0.213 0.321 0.185 ...
 $ V44  : num  0.4256 0.1628 0.2222 0.3202 0.0841 ...
 $ V45  : num  0.2641 0.0621 0.2111 0.4295 0.0692 ...
 $ V46  : num  0.1386 0.0203 0.0176 0.3654 0.0528 ...
 $ V47  : num  0.1051 0.053 0.1348 0.2655 0.0357 ...
 $ V48  : num  0.1343 0.0742 0.0744 0.1576 0.0085 ...
 $ V49  : num  0.0383 0.0409 0.013 0.0681 0.023 0.0264 0.0507 0.0285 0.0777 0.0092 ...
 $ V50  : num  0.0324 0.0061 0.0106 0.0294 0.0046 0.0081 0.0159 0.0178 0.0439 0.0198 ...
 $ V51  : num  0.0232 0.0125 0.0033 0.0241 0.0156 0.0104 0.0195 0.0052 0.0061 0.0118 ...
 $ V52  : num  0.0027 0.0084 0.0232 0.0121 0.0031 0.0045 0.0201 0.0081 0.0145 0.009 ...
 $ V53  : num  0.0065 0.0089 0.0166 0.0036 0.0054 0.0014 0.0248 0.012 0.0128 0.0223 ...
 $ V54  : num  0.0159 0.0048 0.0095 0.015 0.0105 0.0038 0.0131 0.0045 0.0145 0.0179 ...
 $ V55  : num  0.0072 0.0094 0.018 0.0085 0.011 0.0013 0.007 0.0121 0.0058 0.0084 ...
 $ V56  : num  0.0167 0.0191 0.0244 0.0073 0.0015 0.0089 0.0138 0.0097 0.0049 0.0068 ...
 $ V57  : num  0.018 0.014 0.0316 0.005 0.0072 0.0057 0.0092 0.0085 0.0065 0.0032 ...
 $ V58  : num  0.0084 0.0049 0.0164 0.0044 0.0048 0.0027 0.0143 0.0047 0.0093 0.0035 ...
 $ V59  : num  0.009 0.0052 0.0095 0.004 0.0107 0.0051 0.0036 0.0048 0.0059 0.0056 ...
 $ V60  : num  0.0032 0.0044 0.0078 0.0117 0.0094 0.0062 0.0103 0.0053 0.0022 0.004 ...
 $ Class: Factor w/ 2 levels "M","R": 2 2 2 2 2 2 2 2 2 2 ...

ESKNN documentation built on May 2, 2019, 6:25 a.m.