steelplates: Steel Plates Faults Recognition Data

Description Usage Format Source References Examples

Description

These data are the results of an extraction process from images of faults of steel plates. There are seven different faults: Pastry (1), Z_Scratch (2), K_Scratch (3), Stains (4), Dirtiness (5), Bumps (6), Other faults (7).

Usage

1

Format

steelplates is a data frame with 1941 cases (rows) and 28 variables (columns) named:

  1. X_Minimum integer.

  2. X_Maximum integer.

  3. Y_Minimum integer.

  4. Y_Maximum integer.

  5. Pixels_Areas integer.

  6. X_Perimeter integer.

  7. Y_Perimeter integer.

  8. Sum_of_Luminosity integer.

  9. Minimum_of_Luminosity integer.

  10. Maximum_of_Luminosity integer.

  11. Length_of_Conveyer integer.

  12. TypeOfSteel_A300 binary.

  13. TypeOfSteel_A400 binary.

  14. Steel_Plate_Thickness integer.

  15. Edges_Index continuous.

  16. Empty_Index continuous.

  17. Square_Index continuous.

  18. Outside_X_Index continuous.

  19. Edges_X_Index continuous.

  20. Edges_Y_Index continuous.

  21. Outside_Global_Index continuous.

  22. LogOfAreas continuous.

  23. Log_X_Index continuous.

  24. Log_Y_Index continuous.

  25. Orientation_Index continuous.

  26. Luminosity_Index continuous.

  27. SigmoidOfAreas continuous.

  28. Class discrete 1, 2, 3, 4, 5, 6 or 7.

Source

A. Asuncion and D. J. Newman. Uci machine learning repository, 2007. http://archive.ics.uci.edu/ml/.

References

M. Buscema, S. Terzi, W. Tastle. A new meta-classifier. Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, 2010. doi: 10.1109/NAFIPS.2010.5548298.

M. Buscema. MetaNet*: The theory of independent judges. Substance Use & Misuse. 33(2):439-461, 1998. doi: 10.3109/10826089809115875.

Examples

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## Not run: 
data(steelplates)

# Split dataset into train (75

set.seed(3)

Steelplates <- split(p = 0.75, Dataset = steelplates, class = 28)

# Estimate number of components, component weights and component
# parameters for train subsets.

steelplatesest <- REBMIX(model = "REBMVNORM",
  Dataset = a.train(Steelplates),
  Preprocessing = "histogram",
  cmax = 15,
  Criterion = "BIC")

# Classification.

steelplatescla <- RCLSMIX(model = "RCLSMVNORM",
  x = list(steelplatesest),
  Dataset = a.test(Steelplates),
  Zt = a.Zt(Steelplates))

steelplatescla

summary(steelplatescla)

## End(Not run)

rebmix documentation built on July 28, 2021, 5:08 p.m.