Author: Semeion, Research Center of Sciences of Communication, Rome, Italy.
Source: [UCI](http://archive.ics.uci.edu/ml/datasets/steel+plates+faults)
Please cite: Dataset provided by Semeion, Research Center of Sciences of Communication, Via Sersale 117, 00128, Rome, Italy.
__Changes w.r.t. version 1: included one target factor with 7 levels as target variable for the classification. Also deleted the previous 7 binary target variables.__
Steel Plates Faults Data Set
A dataset of steel plates' faults, classified into 7 different types. The goal was to train machine learning for automatic pattern recognition.
The dataset consists of 27 features describing each fault (location, size, ...) and 1 feature indicating the type of fault (on of 7: Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps, Other_Faults). The target is the type of fault.
### Attribute Information
* V1: X_Minimum
* V2: X_Maximum
* V3: Y_Minimum
* V4: Y_Maximum
* V5: Pixels_Areas
* V6: X_Perimeter
* V7: Y_Perimeter
* V8: Sum_of_Luminosity
* V9: Minimum_of_Luminosity
* V10: Maximum_of_Luminosity
* V11: Length_of_Conveyer
* V12: TypeOfSteel_A300
* V13: TypeOfSteel_A400
* V14: Steel_Plate_Thickness
* V15: Edges_Index
* V16: Empty_Index
* V17: Square_Index
* V18: Outside_X_Index
* V19: Edges_X_Index
* V20: Edges_Y_Index
* V21: Outside_Global_Index
* V22: LogOfAreas
* V23: Log_X_Index
* V24: Log_Y_Index
* V25: Orientation_Index
* V26: Luminosity_Index
* V27: SigmoidOfAreas
* target: 7 types of fault as classification target
### Relevant Papers
1.M Buscema, S Terzi, W Tastle, A New Meta-Classifier,in NAFIPS 2010, Toronto (CANADA),26-28 July 2010, 978-1-4244-7858-6/10 ©2010 IEEE
2.M Buscema, MetaNet: The Theory of Independent Judges, in Substance Use & Misuse, 33(2), 439-461,1998