Integration of semi-fuzzy SVDD and CC-Rule method for supplier selection
A model based on semi-fuzzy support vector domain description (semi-fuzzy SVDD) is put forward to address multi-classification problem involved in supplier selection. By preprocessing using semi-fuzzy kernel clustering algorithm, original samples are divided into two subsets: deterministic samples and fuzzy samples. Only the fuzzy samples, rather than all original ones, require expert judgment to decide their categories and are selected as training samples to accomplish SVDD specification. Therefore, the samples preprocessing method can not only decrease experts working strength, but also achieve less computational consumption and better performance of the classifier. Nevertheless, in order to accomplish practical decision making, another condition has to be met: good explanations to the decision. A rule extraction method based on cooperative coevolution algorithm (CCEA), is introduced to achieve the target. To validate the proposed methodology, samples from real world were employed for experiments, with results compared with conventional multi-classification support vector machine approaches and other artificial intelligence techniques. Moreover, in terms of rule extraction, experiments on key parameters, different methods including decompositional and pedagogical ones etc. were also conducted.
supplier selection support vector domain description semi-fuzzy kernel clustering algorithm cooperative coevolution algorithm