2015-01-25T21:42:40Z https://www.openml.org/data/download/1592296/php9xWOpn Public 1 1 276158 **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 32 active steel-plates-fault steel-plates-fault ARFF Class public 2015-01-09T19:17:37Z