Multiple Models Soft-sensing Technique Based on An Improved Weighted Rough Set
Huizhong Yang · Huagong zidonghua ji yibiao · 2010
According to the idea that multi-models could improve the estimated accuracy and generalization,a soft-sensing method with multiple models based on an improved weighted rough set was presented.Weighted rough set was effective for class imbalance learning,but it might result in the drop of the classification precision due to lacking fully consideration of selecting sample weighting function.By introducing label weighting function into weighted rough set,a Bayes decision algorithm based on minimum risk was presented.Meanwhile,AdaBoostM2 algorithm was used to search optimization of sample weighting function and label weighting function.The minimum risk weighted rough set classifier which constructed by optimum parameters,effectively boosts the classification accuracy of classifier and ensures the reliability of sub model.