Notice of RetractionResearch on damage level assessment model based on CGA-SVM

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.

The battlefield damage level assessment is different from other assessment problems because of its characteristics such as small sample, multiple influence factors and high dimension. Thereby, the assessment methods like Bayesian Net (BN), Neutral Net (NN), and multiple indexes comprehensive method can´t be used in this domain. So, a new method must be introduced. Then, Support Vector Mechanism (SVM) which can deal with the small sample, multi-indexes and high dimension events is seemed suitable to this problem. To reduce the complexity of the SVM model, Gauss formula is choose as the kernel function of SVM. The parameter of SVM is optimized by the Cloud Genetic Algorithm (CGA) which can accelerate the GA´s searching rate and keep its search randomly to overcome falling into local optimal solution. The model has been proved to be efficient for damage level assessment via a case study.