Human Impact in Complex Classification of Steel Coils

This work explores different artificial intelligence based alternatives to create automatic classification system based on salience maps of coil surface when heavy unbalanced datasets are considered and where the labels have been assigned by human operators, considering different complex rules. After testing the possibilities of classifier setup process, additional effort was spend create synthetic features based on the characteristics of the salience maps and such features have been used to verify the need for additional check of scores coming from the human operators. Although it is a preliminary result, it provide evidences of the significant impact that confusing scores can have on the classifier final performance.