Notice of RetractionIntegrated evaluation of candidate partners´ achievement of dynamic alliance based on PCA-SVR

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Based on the index system of candidate partners´ achievement of dynamic alliance, a integrated evaluation model is established by using principal component analysis and support vector machine. The method has advantages of accuracy, convenience, reliability and rapidity. Through principal component analysis, we have synthesized numerous criteria, eliminated information overlapping of the samples, and reduced the input dimension of SVM. The method is illustrated through examples, the results obtained by using principal component analysis and support vector machine method are compared with that from neural network method and empirical analysis, and the results show that the conjoint method (PCA-SVR) is more precise and fits better.