The resources for this dataset can be found at https://www.openml.org/d/1504

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.  

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 7 binary features indicating the type of fault (on of 7: Pastry, Z_Scratch, K_Scatch, Stains, Dirtiness, Bumps, Other_Faults). The latter is commonly used as a binary classification target ('common' or 'other' 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  
* V28: Pastry  
* V29: Z_Scratch  
* V30: K_Scatch  
* V31: Stains  
* V32: Dirtiness  
* V33: Bumps  
* Class: Other_Faults  

### 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