From: A predictive noise correction methodology for manufacturing process datasets
Instance # | Attributes | |||||||
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A1 | A2 | A3 | A4 | A5 | A6 | A7 | A8 | |
1 | ν | ν | ν | ν | ν | ν | ν | ν |
2 | ν | ν | ν | ν | ν | ν | ν | ν |
3 | ν | ν | ν | ε | ν | ν | ν | ν |
4 | ν | ν | ν | ν | ν | ν | ε | ε |
5 | ν | ν | ν | ε | ε | ν | ν | ε |
6 | ν | ν | ν | ν | ν | ν | ν | ε |
7 | ν | ν | ν | ν | ν | ν | ε | ε |
8 | ν | ν | ν | ε | ν | ε | ν | ε |
9 | ν | ν | ν | ν | ε | ε | ν | ε |
10 | ν | ν | ν | ν | ν | ε | ν | ε |
11 | ν | ν | ν | ε | ν | ε | ε | ε |
12 | ν | ν | ν | ν | ν | ε | ν | ν |
13 | ν | ν | ν | ν | ν | ε | ν | ε |
14 | ν | ν | ν | ε | ν | ε | ε | ε |
15 | ν | ν | ν | ν | ν | ε | ν | ν |
16 | ν | ν | ε | ν | ν | ε | ν | ν |
17 | ν | ν | ν | ν | ν | ε | ν | ν |
18 | ν | ν | ν | ν | ν | ε | ε | ε |
19 | ν | ν | ν | ν | ν | ε | ε | ε |
20 | ν | ν | ν | ν | ν | ε | ε | ε |
21 | ν | ν | ν | ν | ν | ε | ε | ε |
22 | ν | ν | ν | ν | ν | ε | ν | ε |
23 | ν | ν | ν | ν | ν | ε | ν | ε |
24 | ν | ν | ν | ν | ν | ν | ν | ε |
25 | ν | ν | ν | ν | ν | ν | ν | ε |
26 | ν | ε | ν | ε | ν | ν | ε | ε |
27 | ν | ε | ν | ν | ν | ν | ν | ε |
28 | ν | ε | ν | ε | ν | ν | ν | ε |
29 | ν | ν | ν | ν | ν | ν | ν | ε |
30 | ν | ν | ν | ν | ν | ν | ν | ε |
Percentage ε | 0% | 10% | 3% | 23% | 7% | 53% | 30% | 77% |