glass: Glass dataset

In evclass: Evidential Distance-Based Classification

Glass dataset

Description

This data set contains the description of 214 fragments of glass originally collected for a study in the context of criminal investigation. Each fragment has a measured reflectivity index and chemical composition (weight percent of Na, Mg, Al, Si, K, Ca, Ba and Fe). As suggested by Ripley (1994), 29 instances were discarded, and the remaining 185 were re-grouped in four classes: window float glass (70), window non-float glass (76), vehicle window glass (17) and other (22). The data set was split randomly in a training set of size 89 and a test set of size 96.

Usage

data(glass)

Format

A list with two elements:

x

The 185 x 9 object-attribute matrix.

y

A 185-vector containing the class labels.

References

P. M. Murphy and D. W. Aha. UCI Reposition of machine learning databases. [Machine readable data repository]. University of California, Departement of Information and Computer Science, Irvine, CA.

B.D.Ripley, Flexible nonlinear approaches to classification, in "From Statistics to Neural Networks", V. Cherkassly, J. H. Friedman, and H. Wechsler, Eds., Berlin, Germany: Springer-Verlag, 1994, pp. 105–126.

T. Denoeux. A neural network classifier based on Dempster-Shafer theory. IEEE Trans. on Systems, Man and Cybernetics A, 30(2):131–150, 2000.

Examples

data(glass)
table(glass$y)

evclass documentation built on Nov. 9, 2023, 5:08 p.m.