In many classification problems the data is imbalanced, that is the class priors are different. Here, we consider the classification problem of fatigue crack initiation in automotive camshafts, where this imbalance is significant. The extension techniques of Support Vector Machine (SVM) - the Control Sensitivity (CSSVM) and Adaptive Margin (AMSVM) - which offer different ways of dealing with imbalanced data was investigated. Geometric mean was used to evaluate the performance of the model. The CSSVM has outperformed the AMSVM. The use of different kernels did not produce significant changes in the results. The ratio between the misclassification cost and the training size for each class is very similar, indicating a strong relationship between them.
|Number of pages
|Published - Feb 2001
|Congress on Neural Networks and Applications, Fuzzy Sets and Fuzzy Systems and Evolutionary Computing - Puerto De La Cruz, Tenerife, Canary Islands, Spain
Duration: 11 Feb 2001 → 15 Feb 2001
|Congress on Neural Networks and Applications, Fuzzy Sets and Fuzzy Systems and Evolutionary Computing
|Puerto De La Cruz, Tenerife, Canary Islands
|11/02/01 → 15/02/01