Cylinder Bands
Donated on 7/31/1995
Used in decision tree induction for mitigating process delays known as cylinder bands" in rotogravure printing"
Dataset Characteristics
Multivariate
Subject Area
Physics and Chemistry
Associated Tasks
Classification
Feature Type
Categorical, Integer, Real
# Instances
512
# Features
39
Dataset Information
Additional Information
Here's the abstract from the above reference: ABSTRACT: Machine learning tools show significant promise for knowledge acquisition, particularly when human expertise is inadequate. Recently, process delays known as cylinder banding in rotogravure printing were substantially mitigated using control rules discovered by decision tree induction. Our work exemplifies a more general methodology which transforms the knowledge acquisition task from one in which rules are directly elicited from an expert, to one in which a learning system is responsible for rule generation. The primary responsibilities of the human expert are to evaluate the merits of generated rules, and to guide the acquisition and classification of data necessary for machine induction. These responsibilities require the expert to do what an expert does best: to exercise his or her expertise. This seems a more natural fit to an expert's capabilities than the requirements of traditional methodologies that experts explicitly enumerate the rules that they employ.
Has Missing Values?
Yes
Variables Table
| Variable Name | Role | Type | Description | Units | Missing Values |
|---|---|---|---|---|---|
| timestamp | Feature | Integer | no | ||
| cylinder number | Feature | Categorical | no | ||
| customer | Feature | Categorical | no | ||
| job number | Feature | Integer | no | ||
| grain screened | Feature | Categorical | no | ||
| ink color | Feature | Categorical | no | ||
| proof on ctd ink | Feature | Categorical | no | ||
| blade mfg | Feature | Categorical | no | ||
| cylinder division | Feature | Categorical | no | ||
| paper type | Feature | Categorical | no |
0 to 10 of 40
Additional Variable Information
1. timestamp: numeric;19500101 - 21001231 2. cylinder number: nominal 3. customer: nominal; 4. job number: nominal; 5. grain screened: nominal; yes, no 6. ink color: nominal; key, type 7. proof on ctd ink: nominal; yes, no 8. blade mfg: nominal; benton, daetwyler, uddeholm 9. cylinder division: nominal; gallatin, warsaw, mattoon 10. paper type: nominal; uncoated, coated, super 11. ink type: nominal; uncoated, coated, cover 12. direct steam: nominal; use; yes, no * 13. solvent type: nominal; xylol, lactol, naptha, line, other 14. type on cylinder: nominal; yes, no 15. press type: nominal; use; 70 wood hoe, 70 motter, 70 albert, 94 motter 16. press: nominal; 821, 802, 813, 824, 815, 816, 827, 828 17. unit number: nominal; 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 18. cylinder size: nominal; catalog, spiegel, tabloid 19. paper mill location: nominal; north us, south us, canadian, scandanavian, mid european 20. plating tank: nominal; 1910, 1911, other 21. proof cut: numeric; 0-100 22. viscosity: numeric; 0-100 23. caliper: numeric; 0-1.0 24. ink temperature: numeric; 5-30 25. humifity: numeric; 5-120 26. roughness: numeric; 0-2 27. blade pressure: numeric; 10-75 28. varnish pct: numeric; 0-100 29. press speed: numeric; 0-4000 30. ink pct: numeric; 0-100 31. solvent pct: numeric; 0-100 32. ESA Voltage: numeric; 0-16 33. ESA Amperage: numeric; 0-10 34. wax: numeric ; 0-4.0 35. hardener: numeric; 0-3.0 36. roller durometer: numeric; 15-120 37. current density: numeric; 20-50 38. anode space ratio: numeric; 70-130 39. chrome content: numeric; 80-120 40. band type: nominal; class; band, no band *
Dataset Files
| File | Size |
|---|---|
| bands.data | 100.9 KB |
| bands.names | 3.4 KB |
| Index | 117 Bytes |
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset cylinder_bands = fetch_ucirepo(id=32) # data (as pandas dataframes) X = cylinder_bands.data.features y = cylinder_bands.data.targets # metadata print(cylinder_bands.metadata) # variable information print(cylinder_bands.variables)
Evans, B. (1994). Cylinder Bands [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C50C7B.
Creators
Bob Evans
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.