Saad Bashir Alvi, Robert Martin and Johannes Gottschling, Duisburg-Essen University, Germany

This research study proposes a novel method for automatic fault prediction from foundry data introducing the so-called Meta Prediction Function (MPF). Kernel Principal Component Analysis (KPCA) is used for dimension reduction. Different algorithms are used for building the MPF such as Multiple Linear Regression (MLR), Adaptive Neuro Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Neural Network (NN). We used classical machine learning methods such as ANFIS, SVM and NN for comparison with our proposed MPF. Our empirical results show that the MPF consistently outperform the classical methods.

Fuzzy Inference System, ANFIS, Neural Network, Support Vector Machine, KPCA