Comparison of different classification methods on castabilty data coming from steelmaking practice

The problem of the prediction of a critical situation during continuous casting in common steelmaking practice is faced through different traditional soft–computing techniques: the task is to divide the data in two classes corresponding to good and bad casting behaviour respectively. Moreover, a novel algorithm, the Model–based method, is presented. The performance obtained by the different techniques on the real data coming from an important steelmaking industry are compared.