Hematite, as a typical alteration mineral, plays a very important role in uranium exploration. Traditional modeling method usually treats every feature with the same probability. However, this does not hold in many real world applications, which may also cause the reduction of the accuracy of prediction. We propose a novel method called WKNN-SMOreg, which weights the features according to the association of their attributes on the hybrid of KNN and SMOreg. In this way, the error caused by the features with lower association will be reduced. The experiment results show, compared with KNN, SVM and KNN-SMOreg, the novel method improves the accuracy of prediction, and reduces the negative impact of the noise, which also implies that the new method can be well applied in the prediction of alteration minerals.
|Number of pages||10|
|Journal||Journal of Basic Science and Engineering|
|Publication status||Published - 2011|