The crisis early warning of the quality of supply chain based on rough set&feature weighted support vector machine

Abstract

A Rough Set&Feature Weighted Support Vector Machine(RS-FWSVM) model is proposed for the quality of supply chain crisis early-warning, which aims at some problems of the quality of supply chain. This model combines the advantages of the RS and FWSVM, which can get classification per-formances by changing the weights of different linear functions in the feature space. Application process of this model to the crisis early warning of SCQ is researched, which can help enable chain enterprises to identify crises in the process of operations and to predict possible crises.